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A study on identifying synergistic prevention and control regions for PM2.5 and O3 and exploring their spatiotemporal dynamic in China
... With the implementation of a series of environmental regulations in China, PM 2.5 pollution has been significantly improved [1]. However, O 3 concentration has been increasing gradually [2], and the phenomenon of PM 2.5 -O 3 synergistic pollution has appeared increasingly prominent [3,4]. ...
Air pollution, especially the synergistic pollution of PM2.5 and O3, poses a severe threat to human life and production. The synergistic formation mechanism of PM2.5 and O3 pollution is relatively confirmed, while research on their spatiotemporal synergy is urgent. Based on remotely sensed interpretation data from 90 cities in the Yellow River Basin, we analyzed the synergistic evolution of PM2.5 and O3 concentrations during 2013–2020. Combined with the environmental Kuznets curve, we performed factor analysis using a panel regression model. The synergistic evolution pattern shows a gradual decrease in PM2.5 concentration and a gradual increase in O3 concentration. There is a strong spatial auto-correlation in the two pollutants’ concentrations. The relationship between economy and PM2.5 concentration shows an “N-shaped” curve, while that between O3 concentration and economic development presents an inverse “N-shaped” curve. The environmental Kuznets curve shows that the deterioration of O3 pollution takes place later than the mitigation of PM2.5 pollution. Various factors have obvious heterogeneous effects on PM2.5 and O3 concentrations. Meanwhile, the sensitivity effect of per capita GDP on PM2.5 concentration in the midstream region is stronger than that in the upstream region, while the sensitivity effect of per capita GDP on O3 concentration is strongest in the midstream region than that in upstream and downstream region.
... In particular, the correlation between fine particulate matter (PM 2.5 ) and lung cancer is very significant (Feng et al. 2021;Hill et al. 2023;Zhang et al. 2023). PM 2.5 has become one of the prominent influential factors of lung cancer (Hvidtfeldt et al. 2021;Wu et al. 2023;Guo et al. 2023a). ...
Air pollution generated by urbanization and industrialization poses a significant negative impact on public health. Particularly, fine particulate matter (PM2.5) has become one of the leading causes of lung cancer mortality worldwide. The relationship between air pollutants and lung cancer has aroused global widespread concerns. Currently, the spatial agglomeration dynamic of lung cancer incidence (LCI) has been seldom discussed, and the spatial heterogeneity of lung cancer’s influential factors has been ignored. Moreover, it is still unclear whether different socioeconomic levels and climate zones exhibit modification effects on the relationship between PM2.5 and LCI. In the present work, spatial autocorrelation was adopted to reveal the spatial aggregation dynamic of LCI, the emerging hot spot analysis was introduced to indicate the hot spot changes of LCI, and the geographically and temporally weighted regression (GTWR) model was used to determine the affecting factors of LCI and their spatial heterogeneity. Then, the modification effects of PM2.5 on the LCI under different socioeconomic levels and climatic zones were explored. Some findings were obtained. The LCI demonstrated a significant spatial autocorrelation, and the hot spots of LCI were mainly concentrated in eastern China. The affecting factors of LCI revealed an obvious spatial heterogeneity. PM2.5 concentration, nighttime light data, 2 m temperature, and 10 m u-component of wind represented significant positive effects on LCI, while education-related POI exhibited significant negative effects on LCI. The LCI in areas with low urbanization rates, low education levels, and extreme climate conditions was more easily affected by PM2.5 than in other areas. The results can provide a scientific basis for the prevention and control of lung cancer and related epidemics.
In response to the severe air pollution issue, the Chinese government implemented two phases (Phase I, 2013-2017; Phase II, 2018-2020) of clean air actions since 2013, resulting in a significant decline in fine particles (PM2.5) during 2013-2020, while the warm-season (April-September) mean maximum daily 8 h average ozone (MDA8 O3) increased by 2.6 μg m-3 yr-1 in China during the same period. Here, we derived the drivers behind the rising O3 concentrations during the two phases of clean air actions by using a bottom-up emission inventory, a regional chemical transport model, and a multiple linear regression model. We found that both meteorological variations (3.6 μg m-3) and anthropogenic emissions (6.7 μg m-3) contributed to the growth of MDA8 O3 from 2013 to 2020, with the changes in anthropogenic emissions playing a more important role. The anthropogenic contributions to the O3 rise during 2017-2020 (1.2 μg m-3) were much lower than that in 2013-2017 (5.2 μg m-3). The lack of volatile organic compound (VOC) control and the decline in nitrogen oxides (NOx) emissions were responsible for the O3 increase in 2013-2017 due to VOC-limited regimes in most urban areas, while the synergistic control of VOC and NOx in Phase II initially worked to mitigate O3 pollution during 2018-2020, although its effectiveness was offset by the penalty of PM2.5 decline. Future mitigation efforts should pay more attention to the simultaneous control of VOC and NOx to improve O3 air quality.
Air pollution remains a major threat to cardiovascular health and most acute myocardial infarction (AMI) deaths occur at home. However, currently established knowledge on the deleterious effect of air pollution on AMI has been limited to routinely monitored air pollutants and overlooked the place of death. In this study, we examined the association between short-term residential exposure to China’s routinely monitored and unmonitored air pollutants and the risk of AMI deaths at home. A time-stratified case-crossover analysis was undertaken to associate short-term residential exposure to air pollution with 0.1 million AMI deaths at home in Jiangsu Province (China) during 2016–2019. Individual-level residential exposure to five unmonitored and monitored air pollutants including PM1 (particulate matter with an aerodynamic diameter ≤ 1 μm) and PM2.5 (particulate matter with an aerodynamic diameter ≤ 2.5 μm), SO2 (sulfur dioxide), NO2 (nitrogen dioxide), and O3 (ozone) was estimated from satellite remote sensing and machine learning technique. We found that exposure to five air pollutants, even below the recently released stricter air quality standards of the World Health Organization (WHO), was all associated with increased odds of AMI deaths at home. The odds of AMI deaths increased by 20% (95% confidence interval: 8 to 33%), 22% (12 to 33%), 14% (2 to 27%), 13% (3 to 25%), and 7% (3 to 12%) for an interquartile range increase in PM1, PM2.5, SO2, NO2, and O3, respectively. A greater magnitude of association between NO2 or O3 and AMI deaths was observed in females and in the warm season. The greatest association between PM1 and AMI deaths was found in individuals aged ≤ 64 years. This study for the first time suggests that residential exposure to routinely monitored and unmonitored air pollutants, even below the newest WHO air quality standards, is still associated with higher odds of AMI deaths at home. Future studies are warranted to understand the biological mechanisms behind the triggering of AMI deaths by air pollution exposure, to develop intervention strategies to reduce AMI deaths triggered by air pollution exposure, and to evaluate the cost-effectiveness, accessibility, and sustainability of these intervention strategies.
Graphical abstract
Urban ozone (O3) pollution in the atmosphere has become increasingly prominent on a national scale in mainland China, although the atmospheric particulate matter pollution has been significantly reduced in recent years. The clustering and dynamic variation characteristics of the O3 concentrations in cities across the country, however, have not been accurately explored at relevant spatiotemporal scales. In this study, a standard deviational ellipse analysis and multiscale geographically weighted regression models were applied to explore the migration process and influencing factors of O3 pollution based on measured data from urban monitoring sites in mainland China. The results suggested that the urban O3 concentration in mainland China reached its peak in 2018, and the annual O3 concentration reached 157 ± 27 μg/m3 from 2015 to 2020. On the scale of the whole Chinese mainland, the distribution of O3 exhibited spatial dependence and aggregation. On the regional scale, the areas of high O3 concentrations were mainly concentrated in Beijing-Tianjin-Hebei, Shandong, Jiangsu, Henan, and other regions. In addition, the standard deviation ellipse of the urban O3 concentration covered the entire eastern part of mainland China. Overall, the geographic center of ozone pollution has a tendency to move to the south with the time variation. The interaction between sunshine hours and other factors (precipitation, NO2, DEM, SO2, PM2.5) significantly affected the variation of urban O3 concentration. In Southwest China, Northwest China, and Central China, the suppression effect of vegetation on local O3 was more obvious than that in other regions. Therefore, this study clarified for the first time the migration path of the gravity center of the urban O3 pollution and identified the key areas for the prevention and control of O3 pollution in mainland China.
Increasing surface ozone (O3) concentrations has emerged as a key air pollution problem in many urban regions worldwide in the last decade. A longstanding major issue in tackling ozone pollution is the identification of the O3 formation regime and its sensitivity to precursor emissions. In this work, we propose a new transformed empirical kinetic modeling approach (EKMA) to diagnose the O3 formation regime using regulatory O3 and NO2 observation datasets, which are easily accessible. We demonstrate that mapping of monitored O3 and NO2 data on the modeled regional O3-NO2 relationship diagram can illustrate the ozone formation regime and historical evolution of O3 precursors of the region. By applying this new approach, we show that for most urban regions of China, the O3 formation is currently associated with a volatile organic compound (VOC)-limited regime, which is located within the zone of daytime-produced O3 (DPO3) to an 8h-NO2 concentration ratio below 8.3 ([DPO3]/[8h-NO2] ≤ 8.3). The ozone production and controlling effects of VOCs and NOx in different cities of China were compared according to their historical O3-NO2 evolution routes. The approach developed herein may have broad application potential for evaluating the efficiency of precursor controls and further mitigating O3 pollution, in particular, for regions where comprehensive photochemical studies are unavailable.
Surface ozone (O3), one of the harmful air pollutants, generated significantly negative effects on human health and plants. Existing O3 datasets with coarse spatiotemporal resolution and limited coverage, and the uncertainties of O3 influential factors seriously restrain related epidemiology and air pollution studies. To tackle above issues, we proposed a novel scheme to estimate daily O3 concentrations on a fine grid scale (1 km × 1 km) from 2018 to 2020 across China based on machine learning methods using hourly observed ground-level pollutant concentrations data, meteorological data, satellite data, and auxiliary data including digital elevation model (DEM), land use data (LUD), normalized difference vegetation index (NDVI), population (POP), and nighttime light images (NTL), and to identify the difference of influential factors of O3 on diverse urbanization and topography conditions. Some findings were achieved. The correlation coefficients (R²) between O3 concentrations and surface net solar radiation (SNSR), boundary layer height (BLH), 2 m temperature (T2M), 10 m v-component (MVW), and NDVI were 0.80, 0.40, 0.35, 0.30, and 0.20, respectively. The random forest (RF) demonstrated the highest validation R² (0.86) and lowest validation RMSE (13.74 μg/m³) in estimating O3 concentrations, followed by support vector machine (SVM) (R² = 0.75, RMSE = 18.39 μg/m³), backpropagation neural network (BP) (R² = 0.74, RMSE = 19.26 μg/m³), and multiple linear regression (MLR) (R² = 0.52, RMSE = 25.99 μg/m³). Our China High-Resolution O3 Dataset (CHROD) exhibited an acceptable accuracy at different spatial–temporal scales. Additionally, O3 concentrations showed decreasing trend and represented obviously spatiotemporal heterogeneity across China from 2018 to 2020. Overall, O3 was mainly affected by human activities in higher urbanization regions, while O3 was mainly controlled by meteorological factors, vegetation coverage, and elevation in lower urbanization regions. The scheme of this study is useful and valuable in understanding the mechanism of O3 formation and improving the quality of the O3 dataset.
High spatial resolution PM2.5 data covering a long time period are urgently needed to support population exposure assessment and refined air quality management. In this study, we provided complete-coverage PM2.5 predictions with a 1 km spatial resolution from 2000 to the present under the Tracking Air Pollution in China (TAP, http://tapdata.org.cn/, last access: 3 October 2022) framework. To support high spatial resolution modeling, we collected PM2.5 measurements from both national and local monitoring stations. To correctly reflect the temporal variations in land cover characteristics that affected the local variations in PM2.5, we constructed continuous annual geoinformation datasets, including the road maps and ensemble gridded population maps, in China from 2000 to 2021. We also examined various model structures and predictor combinations to balance the computational cost and model performance. The final model fused 10 km TAP PM2.5 predictions from our previous work, 1 km satellite aerosol optical depth retrievals, and land use parameters with a random forest model. Our annual model had an out-of-bag R2 ranging between 0.80 and 0.84, and our hindcast model had a by-year cross-validation R2 of 0.76. This open-access, 1 km resolution PM2.5 data product, with complete coverage, successfully revealed the local-scale spatial variations in PM2.5 and could benefit environmental studies and policymaking.
Climate change mitigation measures can yield substantial air quality improvements while emerging clean air measures in developing countries can also lead to CO 2 emission mitigation co-benefits by affecting the local energy system. Here, we evaluate the effect of China’s stringent clean air actions on its energy use and CO 2 emissions from 2013-2020. We find that widespread phase-out and upgrades of outdated, polluting, and inefficient combustion facilities during clean air actions have promoted the transformation of the country’s energy system. The co-benefits of China’s clean air measures far outweigh the additional CO 2 emissions of end-of-pipe devices, realizing a net accumulative reduction of 2.43 Gt CO 2 from 2013-2020, exceeding the accumulated CO 2 emission increase in China (2.03 Gt CO 2 ) during the same period. Our study indicates that China’s efforts to tackle air pollution induce considerable climate benefit, and measures with remarkable CO 2 reduction co-benefits deserve further attention in future policy design.
Rationale:
Extremes of heat and particulate air pollution threaten human health and are becoming more frequent due to climate change. Understanding health impacts of co-exposure to extreme heat and air pollution is urgent.
Objectives:
To estimate association of acute co-exposure to extreme heat and ambient fine particulate matter (PM2.5) with all-cause, cardiovascular, and respiratory mortality in California from 2014-2019.
Methods:
We used a case-crossover study design with time-stratified matching using conditional logistic regression to estimate mortality associations with acute co-exposures to extreme heat and PM2.5. For each case day (date of death) and its control days, daily average PM2.5, maximum and minimum temperature were assigned (0-3-day lag) based on decedent's residence census tract.
Main results:
All-cause mortality risk increased 6.1% (95%confidence interval, CI: 4.1, 8.1) on extreme maximum temperature only days and 5.0% (95%CI: 3.0, 8.0) on extreme PM2.5 only days, compared to non-extreme days. Risk increased 21.0% (95%CI: 6.6, 37.3) on days with exposure to both extreme maximum temperature and PM2.5. Increased risk of cardiovascular and respiratory mortality on extreme co-exposure days was 29.9% (95%CI: 3.3, 63.3) and 38.0% (95%CI:-12.5, 117.7), respectively, and were more than the sum of individual effects of extreme temperature and PM2.5 only. A similar pattern was observed for co-exposure to extreme PM2.5 and minimum temperature. Effect estimates were larger over age 75 years.
Conclusion:
Short-term exposure to extreme heat and air pollution alone were individually associated with increased risk of mortality, but their co-exposure had larger effects beyond the sum of their individual effects. This article is open access and distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).
Plenty of epidemiological approaches have been explored to detect the effects of environmental and socioeconomic factors on acute myocardial infarction (AMI) mortality. Whereas, identifying the influence of potential affecting factors on AMI mortality based on a spatial epidemiological perspective was strongly desired. Moreover, the interaction effects of two potential factors on the diseases were always neglected previously. Here, the Geodetector and geographically & temporally weighted regression model (GTWR) combined with multi-source spatiotemporal datasets were introduced to quantitatively determine the relationship between AMI mortality and potential influencing factors across Xi’an during 2014–2016. Besides, Moran’s I was adopted to diagnose the spatial autocorrelation of AMI mortality. Some findings were achieved. The number of AMI mortality cases increased from 5075 in 2014 to 6774 in 2016. Air pollutants, meteorological factors, economic status, and topography factors exhibited a significant effect on AMI mortality. The AMI mortality demonstrated an obvious spatial autocorrelation feature during 2014–2016. POP and PE represented the most obvious impact on AMI mortality, respectively. Moreover, the interaction of any two factors was larger than that of the single factor on AMI mortality, and the factors with the strongest interaction vary according to lag groups and ages. The effects of factors on AMI mortality were POP (− 628.925) > PE (140.102) > RD (79.145) > O3 (− 58.438) > E_NH3 (42.370) for male, and POP (− 751.206) > RD (132.935) > E_NH3 (58.758) > PE (− 45.434) > O3 (− 21.256) for female, respectively. This work reminds the local government to continuously control air pollution, strengthen urban planning, and improve the health care of the rural areas for alleviating AMI mortality. Meanwhile, the scheme of the current study supplies a scientific reference for examining the effects of potential impact factors on related diseases using the spatial epidemiological perspective.
The outbreak of Coronavirus disease 2019 (COVID-19) led to the widespread stagnation of urban activities, resulting in a significant reduction in industrial pollution and traffic pollution. This affected how urban form influences air quality. This study reconsiders the influence of urban form on air quality in five urban agglomerations in China during the pandemic period. The random forest algorithm was used to quantitate the urban form–air quality relationship. The urban form was described by urban size, shape, fragmentation, compactness, and sprawl. Air quality was evaluated by the Air Quality Index (AQI) and the concentration of six pollutants (CO, O3, NO2, PM2.5, PM10, SO2). The results showed that urban fragmentation is the most important factor affecting air quality and the concentration of the six pollutants. Additionally, the relationship between urban form and air quality varies in different urban agglomerations. By analyzing the extremely important indicators affecting air pollution, the urban form–air quality relationship in Beijing-Tianjin-Hebei is rather complex. In the Chengdu-Chongqing and the Pearl River Delta, urban sprawl and urban compactness are extremely important indicators for some air pollutants, respectively. Furthermore, urban shape ranks first for some air pollutants both in the Triangle of Central China and the Yangtze River Delta. Based on the robustness test, the performance of the random forest model is better than that of the multiple linear regression (MLR) model and the extreme gradient boosting (XGBoost) model.
Based on the exposure data sets from the Tracking Air Pollution in China (TAP, http://tapdata.org.cn/), we characterized the spatiotemporal variations in PM2.5 and O3 exposures and quantified the long- and short-term exposure related premature deaths during 2013–2020 with respect to the two-stage clean air actions (2013–2017 and 2018–2020). We find a 48% decrease in national PM2.5 exposure during 2013–2020, although the decrease rate has slowed after 2017. At the same time, O3 pollution worsened, with the average April–September O3 exposure increased by 17%. The improved air quality led to 308 thousand and 16 thousand avoided long- and short-term exposure related deaths, respectively, in 2020 compared to the 2013 level, which was majorly attributed to the reduction in ambient PM2.5 concentration. It is also noticed that with smaller PM2.5 reduction, the avoided long-term exposure associated deaths in 2017–2020 (13%) was greater than that in 2013–2017 (9%), because the exposure–response curve is nonlinear. As a result of the efforts in reducing PM2.5-polluted days with the daily average PM2.5 higher than 75 μg/m³ and the considerable increase in O3-polluted days with the daily maximum 8 h average O3 higher than 160 μg/m³, deaths attributable to the short-term O3 exposure were greater than those due to PM2.5 exposure since 2018. Future air quality improvement strategies for the coordinated control of PM2.5 and O3 are urgently needed.
This study is the first to explore the potential associations among allergic conjunctivitis (AC), air pollution, and meteorological conditions in Northeast China. Data of meteorology, ambient atmospheric pollutants, and the incidence of allergic conjunctivitis (IAC) in prefecture-level cities between the years 2014 and 2018 are analyzed. The results show an increasing trend in the AC of average growth rate per annum 7.6%, with the highest incidence in the provincial capitals. The IAC is positively correlated with atmospheric pollutants (i.e., PM2.5, PM10, CO, SO2, NO2, and O3) and meteorological factors (i.e., air temperature and wind speed), but negatively correlated with relative humidity. These results suggest that the IAC is directly proportional to pollution level and climatic conditions, and also the precedence of air pollution. We have further obtained the threshold values of atmospheric pollutants concentration and meteorological factors, a turning point above which more AC may be induced. Compared with the air quality standard advised by China and the World Health Organization (WHO), both thresholds of PM10 (70 μg m−3) and PM2.5 (45 μg m−3) are higher than current standards and pose a less environmental risk for the IAC. SO2 threshold (23 μg m−3) is comparable to the WHO standard and significantly lower than that of China’s, indicating greater environmental risks in China. Both thresholds of NO2 (27 μg m−3) and O3 (88 μg m−3) are below current standards, indicating that they are major environmental risk factors for the IAC. Our findings highlight the importance of atmospheric environmental protection and reference for health-based amendment.
Air pollution has altered the Earth’s radiation balance, disturbed the ecosystem, and increased human morbidity and mortality. Accordingly, a full-coverage high-resolution air pollutant data set with timely updates and historical long-term records is essential to support both research and environmental management. Here, for the first time, we develop a near real-time air pollutant database known as Tracking Air Pollution in China (TAP, http://tapdata.org.cn/) that combines information from multiple data sources, including ground observations, satellite aerosol optical depth (AOD), operational chemical transport model simulations, and other ancillary data such as meteorological fields, land use data, population, and elevation. Daily full-coverage PM2.5 data at a spatial resolution of 10 km is our first near real-time product. The TAP PM2.5 is estimated based on a two-stage machine learning model coupled with the synthetic minority oversampling technique and a tree-based gap-filling method. Our model has an averaged out-of-bag cross-validation R² of 0.83 for different years, which is comparable to those of other studies, but improves its performance at high pollution levels and fills the gaps in missing AOD on daily scale. The full coverage and near real-time updates of the daily PM2.5 data allow us to track the day-to-day variations in PM2.5 concentrations over China in a timely manner. The long-term records of PM2.5 data since 2000 will also support policy assessments and health impact studies. The TAP PM2.5 data are publicly available through our website for sharing with the research and policy communities.
The absence of up-to-date emissions has been a major impediment to
accurately simulating aspects of atmospheric chemistry and to precisely
quantifying the impact of changes in emissions on air pollution. Hence, a
nonlinear joint analytical inversion (Gauss–Newton method) of both volatile
organic compounds (VOCs) and nitrogen oxide (NOx) emissions is made by
exploiting the Smithsonian Astrophysical Observatory (SAO) Ozone Mapping and
Profiler Suite Nadir Mapper (OMPS-NM) formaldehyde (HCHO) and the National
Aeronautics and Space Administration (NASA) Ozone Monitoring Instrument
(OMI) tropospheric nitrogen dioxide (NO2) columns during the
Korea–United States Air Quality (KORUS-AQ) campaign over East Asia in
May–June 2016. Effects of the chemical feedback of NOx and VOCs on both
NO2 and HCHO are implicitly included by iteratively optimizing the
inversion. Emission uncertainties are greatly narrowed (averaging
kernels > 0.8, which is the mathematical presentation of the
partition of information gained from the satellite observations with respect
to the prior knowledge) over medium- to high-emitting areas such as cities
and dense vegetation. The prior amount of total NOx emissions is mainly
dictated by values reported in the MIX-Asia 2010 inventory. After the
inversion we conclude that there is a decline in emissions (before, after, change) for
China (87.94±44.09 Gg d−1, 68.00±15.94 Gg d−1, −23 %), North
China Plain (NCP) (27.96±13.49 Gg d−1, 19.05±2.50 Gg d−1,
−32 %), Pearl River Delta (PRD) (4.23±1.78 Gg d−1, 2.70±0.32 Gg d−1, −36 %), Yangtze River Delta (YRD) (9.84±4.68 Gg d−1,
5.77±0.51 Gg d−1, −41 %), Taiwan (1.26±0.57 Gg d−1,
0.97±0.33 Gg d−1, −23 %), and Malaysia (2.89±2.77 Gg d−1,
2.25±1.34 Gg d−1, −22 %), all of which have effectively implemented
various stringent regulations. In contrast, South Korea (2.71±1.34 Gg d−1, 2.95±0.58 Gg d−1, +9 %) and Japan (3.53±1.71 Gg d−1, 3.96±1.04 Gg d−1, +12 %) are experiencing an increase in
NOx emissions, potentially due to an increased number of diesel vehicles and
new thermal power plants. We revisit the well-documented positive bias (by a
factor of 2 to 3) of MEGAN v2.1 (Model of Emissions of Gases and Aerosols
from Nature) in terms of biogenic VOC emissions in
the tropics. The inversion, however, suggests a larger growth of VOCs (mainly
anthropogenic) over NCP (25 %) than previously reported (6 %) relative
to 2010. The spatial variation in both the magnitude and sign of NOx and
VOC emissions results in nonlinear responses of ozone production and loss. Due
to a simultaneous decrease and increase in NOx∕VOC over NCP and YRD, we
observe a ∼53 % reduction in the ratio of the chemical loss
of NOx (LNOx) to the chemical loss of ROx
(RO2+HO2) over the surface transitioning toward NOx-sensitive regimes, which
in turn reduces and increases the afternoon chemical loss and production of ozone
through NO2+OH (−0.42 ppbv h−1)∕HO2 (and RO2)+NO
(+0.31 ppbv h−1). Conversely, a combined decrease in NOx and
VOC emissions in Taiwan, Malaysia, and southern China suppresses the
formation of ozone. Simulations using the updated emissions indicate
increases in maximum daily 8 h average (MDA8) surface ozone over China
(0.62 ppbv), NCP (4.56 ppbv), and YRD (5.25 ppbv), suggesting that emission
control strategies on VOCs should be prioritized to curb ozone production
rates in these regions. Taiwan, Malaysia, and PRD stand out as regions
undergoing lower MDA8 ozone levels resulting from the NOx reductions
occurring predominantly in NOx-sensitive regimes.
Climate change caused by CO2 emissions (CE) has received widespread global concerns. Obtaining precision CE data is necessary for achieving carbon peak and carbon neutrality. Significant deficiencies of existing CE datasets such as coarse spatial resolution and low precision can hardly meet the actual requirements. An enhanced population-light index (RPNTL) was developed in this study, which integrates the Nighttime Light Digital Number (DN) Value from the National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) and population density to improve C E estimation accuracy. The CE from the Carbon Emission Accounts & Datasets (CEADS) was divided into three sectors, namely urban, industrial, and rural, to differentiate the heterogeneity of CE in each sector. The ordinary least square (OLS), geographically weighted regression (GWR), temporally weighted regression (TWR), and geographically and temporally weighted regression (GTWR) models were employed to establish the quantitative relationship between RPNTL and CE for each sector. The optimal model was determined through model comparison and precision evaluation and was utilized to rasterize CE for urban, industrial, and rural areas. Additionally, hot spot analysis, trend analysis, and standard deviation ellipses were introduced to demonstrate the spatiotemporal dynamic characteristics of CE at multiple scales. The performance of the GTWR outperformed other methods in estimating CE. The enhanced RPNTL demonstrated a higher coefficient of determination (R2 = 0.95) than the NTL (R2 = 0.92) in predicting CE, particularly in rural regions where the R2 value increased from 0.76 to 0.81. From 2013 to 2019, high CE was observed in eastern and northern China, while a decreasing trend was detected in northeastern China and Chengdu-Chongqing. Conversely, the Yangtze River Delta, Pearl River Delta, Fenwei Plain, and Henan Province showed an increasing trend. The center of gravity for industrial and rural CE is shifting towards western regions, whereas that for urban CE is moving northward. This study provides valuable insights for decision-making on CE control.
There is a growing need to apply geospatial artificial intelligence analysis to disparate environmental datasets to find solutions that benefit frontline communities. One such critically needed solution is the prediction of health-relevant ambient ground-level air pollution concentrations. However, many challenges exist surrounding the size and representativeness of limited ground reference stations for model development, reconciling multi-source data, and interpretability of deep learning models. This research addresses these challenges by leveraging a strategically deployed, extensive low-cost sensor (LCS) network that was rigorously calibrated through an optimized neural network. A set of raster predictors with varying data quality and spatial scales was retrieved and processed, including gap-filled satellite aerosol optical depth products and airborne LiDAR-derived 3D urban form. We developed a multi-scale, attention-enhanced convolutional neural network model to reconcile the LCS measurements and multi-source predictors for estimating daily PM2.5 concentration at 30-m resolution. This model employs an advanced approach by using the geostatistical Kriging method to generate a baseline pollution pattern and a multi-scale residual method to identify both regional patterns and localized events for high-frequency feature retention. We further used permutation tests to quantify the feature importance, which has rarely been done in DL applications in environmental science. Finally, we demonstrated one application of the model by investigating the air pollution inequality issue across and within various urbanization levels at the block group scale. Overall, this research demonstrates the potential of geospatial AI analysis to provide actionable solutions for addressing critical environmental issues.
Ozone (O3) pollution in the atmosphere is getting worse in many cities. In order to improve the accuracy of O3 prediction and obtain the spatial distribution of O3 concentration over a continuous period of time, this paper proposes a VAR-XGBoost model based on Vector autoregression (VAR), Kriging method and XGBoost (Extreme Gradient Boosting). China is used as an example and its spatial distribution of O3 is simulated. In this paper, the O3 concentration data of the monitoring sites in China are obtained, and then a spatial prediction method of O3 mass concentration based on the VAR-XGBoost model is established, and finnally its influencing factors are analyzed. This paper concludes that O3 features the highest correlation with PM2.5 and the lowest correlation with SO2. Among the measurement factors, wind speed and temperature are the most important factors affecting O3 pollution, which are positively correlated to O3 pollution. In addition, precipitation is negatively correlated with 8-hour ozone concentration. In this paper, the performance of the VAR-XGBoost model is evaluated based on the ten-fold cross-validation method of sample, site and time, and a comparison with the results of XGBoost, CatBoost (categorical boosting), ExtraTrees, GBDT (gradient boosting decision tree), AdaBoost (adaptive boosting), RF (random forest), Decision tree, and LightGBM (light gradient boosting machine) models is conducted. The result shows that the prediction accuracy of the VAR-XGBoost model is better than other models. The seasonal and annual average R2 reaches 0.94 (spring), 0.93 (summer), 0.92 (autumn), 0.93 (winter), and 0.95 (average from 2016 to 2021). The data show that the applicability of the VAR-XGBoost model in simulating the spatial distribution of O3 concentrations in China performs well. The spatial distribution of O3 concentrations in the Chinese region shows an obvious feature of high in the east and low in the west, and the spatial distribution is strongly influenced by topographical factors. The mean concentration is clearly low in winter and high in summer within a season. The results of this study can provide a scientific basis for the prevention and control of regional O3 pollution in China, and can also provide new ideas for the acquisition of data on the spatial distribution of O3 concentrations within cities.
Presence of particulate matters with aerodynamic diameter of less than 2.5 μm (PM2.5) in the atmosphere is fast increasing in Malaysia due to industrialization and urbanization. Prolonged exposure of PM2.5 can cause serious health effects to human. This research is aimed to identify the most reliable model to predict the PM2.5 pollution using multi-layered feedforward-backpropagation neural network (FBNN). Air quality and meteorological data were collected from Department of Environment (DOE) Malaysia. Six different training algorithms consisting of thirteen various training functions were trained and compared. FBNN model with the highest coefficient correlation (R2) and lowest root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were selected as the best performing model. Levenberg Marquardt (trainlm) is the best performing algorithms compared to other algorithms with R2 value of 0.9834 and the lowest error values for RMSE (2.3981), MAE (1.7843) and MAPE (0.1063).
PM2.5 and ozone (O3) are two major air pollutants in China. A systematic evaluation of the spatiotemporal variations in the correlation between them is essential for their coordinated prevention and control. In this study, we investigated the spatiotemporal variations in the correlations between the surface PM2.5 and O3 concentrations, and the influences of the meteorological parameters in eastern China were analyzed. The major findings of this study are as follows. From 2015 to 2019, the annual number of PM2.5 (O3) pollution days in eastern China decreased (increased) by 8.6% (19.2%). In the three developed regions, the O3 concentrations increased with increasing PM2.5 when the PM2.5 concentrations were less than ∼50 μg m⁻³; while the O3 concentrations decreased with increasing PM2.5 when the PM2.5 concentrations were greater than ∼50 μg m⁻³, except in the Pearl River Delta. The daily average PM2.5 concentrations were positively correlated with the O3 concentrations in most regions and seasons in eastern China, but they were negatively correlated with the O3 concentrations in the cold season, especially in the NCP. The correlation between these two pollutants exhibited notable north-south and seasonal variations, and it tended to be more positively correlated as the PM2.5 concentration decreased. Further investigation revealed that the temperature was positively correlated with the O3 concentration, while they exhibited different seasonal variations with the PM2.5 concentration. The relative humidity was negatively correlated with the O3 concentration and positively correlated with the PM2.5 concentration in the NCP, but was negatively correlated with it in the YRD and PRD. The temperature was a significant meteorological parameter influencing the correlation between the PM2.5 and O3 concentrations, with a coefficient of determination (R²) of 0.83. Under high-temperature conditions, the PM2.5 and O3 concentrations exhibited a stronger positive correlation. These results provide guidance for coordinating PM2.5 and O3 pollution prevention and control in eastern China.
Enhanced weathering (EW) of minerals could potentially absorb atmospheric CO2 at gigaton scale per year and store it as bicarbonate and carbonate in the ocean. However, this process must be accelerated by engineered reactors, in which optimal reaction conditions maximise the CO2 capture rate and minimise the energy and water consumption. In this work, trickle beds (TBs) and packed bubble columns (PBCs), operated with fresh water and CO2 -rich flue gas, are chosen as typical chemical reactors to perform the EW-based CO2 capture. We firstly develop experimentally validated physics-based mechanistic models then generate data to train data-driven surrogate models to achieve rapid prediction of performance and multi-variable optimization. Two surrogate models, namely, response surface methodology (RSM) and extended adaptive hybrid functions (E-AHF), are developed and compared, in which the effect of five design variables on three objective functions are investigated. Results show that the R² for the prediction of CO2 capture rate (CR) and water consumption (WC) through RSM and E-AHF is higher than 0.84. For TB reactors, in particular, the calculated R² is higher than 0.96. The prediction accuracy of energy consumption (EC) through the RSM approach is, however, relatively poor (R² ∼ 0.79), but is improved by using the E-AHF surrogate model, increasing to R² ∼ 0.89. The developed data-driven surrogate model can rapidly predict the performance indicators of TB and PBC reactors without solving complex mechanistic models consisting of many partial differential equations. After optimization using the surrogate models, improvements were achieved in the objectives for TB and PBC reactors as follows: CR increased by 37.8% and 13.1%, EC reduced by 37.4% and 23.8%, and WC reduced by 12.5% and 40.7%, respectively.
Cyanobacterial blooms in most lakes exhibit extraordinary changes in time and space. Herein, a cyanobacterial prediction model was designed for Lake Taihu based on a machine learning method. This method can generate temporally continuous (24 moments throughout the day) cyanobacterial data at a fine spatial scale of 9 km. The hourly meteorological data for 24 moments of the day were obtained from ERA5-Land data. Areal coverage of cyanobacterial blooms was derived from the hourly Geostationary Ocean Color Imager reflectance data observed only eight times a day (from ~8:00 to ~15:00, UTC+8). The cyanobacterial and meteorological data of eight moments in Lake Taihu from 2011 to 2020 were used to design the prediction model. The results were compared and validated employing nine training strategies to determine the best cyanobacterial prediction model for Lake Taihu (R = 0.42; root mean square error = 0.10). With the best-fitted model utilizing meteorological data (2011-2020), the area coverage of cyanobacterial blooms at the other 16 moments during a day were estimated. Based on this, the regional and temporal characteristics of diurnal bloom variation were evaluated at an hourly scale. The results indicated that the hourly variations in the areal coverage of cyanobacterial blooms at 24 moments of the day had similar patterns in each subregion of Lake Taihu with minor seasonal variations. The six meteorological variables adopted to construct the model had similar diurnal changes but with diverse value ranges among the seasons. Further analysis revealed that three meteorological variables (temperature, surface pressure, and evaporation) were positively related to diurnal bloom variations at an hourly scale. Overall, these results illustrate that meteorological conditions can affect the occurrence of cyanobacterial blooms at multiple time scales (e.g., hourly, daily, or monthly). The developed cyanobacterial prediction model can provide cyanobacterial data when cyanobacterial data is unavailable for the target waterbody.
High PM2.5 concentration threats ecosystem functions but limited quantitative studies have recognized PM2.5 pollution as an individual stressor in evaluating ecological risk. In this study, we applied a machine-learning-based simulation model incorporating full-coverage satellite-driven PM2.5 dataset to estimate high-resolution ground PM2.5 concentration for the Golden Triangle of Southern Fujian Province, China (GTSF) in 2030 under two Representative Concentration Pathways (RCPs). Based on the simulation output, the ecological risk's spatiotemporal change and the risk for different land cover types, which were caused by PM2.5 pollution, were assessed. We found that the PM2.5 levels and ecological risk in the GTSF under RCP 4.5 would be reduced while those under RCP 8.5 would continue to increase. The regions with the highest ecological risk under RCP 4.5 are the most urbanized and industrialized districts, while those with the highest ecological risk under RCP 8.5 are of the highest rate in urbanization and the greatest decrease in planetary potential layer height. For both base years and 2030 under two RCPs, the ecological risk on developed land is the highest, while that on the forest is the lowest. Our study can provide useful information for environmental policy risk assessment.
Particulate matter (PM) pollution greatly endanger human physical and mental health, and it is of great practical significance to predict PM concentrations accurately. This study measured one-year monitoring data of six main meteorological parameters and PM2.5 concentrations independently at two monitoring sites in central China's Hunan Province. These datasets were then employed to train, validate, and evaluate the proposed extreme gradient boosting (XGBoost) machine learning model and the fully connected neural network deep learning model, respectively. The performances of the two models were compared, analyzed, and optimized through model parameter tuning. The XGBoost model had better prediction ability with R² higher than 0.761 in the complete test dataset. When the complete dataset was divided into stratified sub-sets by daytime-nighttime periods, the value of R² increased to 0.856 in the nighttime test dataset. The feature importance and influential mechanism of meteorological variables on PM2.5 concentrations were analyzed and discussed.
Air pollutants, especially ambient particulate matter (PM2.5), detrimentally impact human health and cause premature deaths. The dynamic characteristics and associated health risks of PM2.5 are analyzed based on the standard deviational ellipse (SDE) and trend analysis in Saudi Arabia (SAU) from 1998 to 2018 by utilizing recently updated satellite-derived PM2.5 concentrations (V4.GL.03). The outcomes show that the national average PM2.5 concentration increased from 28μg/m³ to 45μg/m³ with a growth rate of 2.3μg/m³/year. The center of median PM2.5 concentrations moved to the southeast over the years studied due to the presence of vast sandy deserts, sand dunes, a busy port, and coastal and industrial areas in this region. The areas of SAU that experienced PM2.5 concentrations above 35μg/m³ increased from 20% to 70%. The rapid-fast growth (RFG) class acquired from the unsupervised classification has the fastest growth rate of 2.5 μg/m³/yr, occurring in southeastern SAU, namely Ash-Sharqiyah, Ar-Riyad, and Najran. It covered ∼27% of the total area of SAU over the study period. Whereas, the slow growth (SG) class with a less than 0.2 μg/m³/yr growth rate covered 12% of the total area of SAU, distributed in northwestern regions. The extent of extremely-high risk areas corresponding to greater than 1 × 10³ μg·person/m³ increased from 4% to 11%, particularly in Makkah, Central Al-Madinah, and western Asir, Jizan, mid-eastern Najran, Al-Quassim, and mid-eastern Ar-Riyad and Ash Sharqiyah.
Generating a long-term high-spatiotemporal resolution global PM2.5 dataset is of great significance for environmental management to mitigate the air pollution concerns worldwide. However, the current long-term (2003–2020) global reanalysis dataset Copernicus Atmosphere Monitoring Service (CAMS) reanalysis has drawbacks in fine-scale research due to its coarse spatiotemporal resolution (0.75°, 3-h). Hence, this paper developed a deep learning-based framework (DeepCAMS) to downscale CAMS PM2.5 product on the spatiotemporal dimension for resolution enhancement. The nonlinear statistical downscaling from low-resolution (LR) to high-resolution (HR) data can be learned from the high quality (0.25°, hourly) but short-term (2018–2020) Goddard Earth Observing System composition forecast (GEOS-CF) system PM2.5 product. Compared to the conventional spatiotemporal interpolation methods, simulation validations on GEOS-CF demonstrate that DeepCAMS is capable of producing accurate temporal variations with an improvement of Root-Mean-Squared Error (RMSE) of 0.84 (4.46 to 5.30) ug/m³ and spatial details with an improvement of Mean Absolute Error (MAE) of 0.16 (0.34 to 0.50) ug/m³. The real validations on CAMS reflect convincing spatial consistency and temporal continuity at both regional and global scales. Furthermore, the proposed dataset is validated with OpenAQ air quality data from 2017 to 2019, and the in-situ validations illustrate that the DeepCAMS maintains the consistent precision (R: 0.597) as the original CAMS (R: 0.593) while tripling the spatiotemporal resolution. The proposed dataset will be available at https://doi.org/10.5281/zenodo.6381600.
How to win the “Blue Sky Protection Campaign” is becoming the focus all over the world, especially in developing economies, while the implementation of the smart cities initiative (SCI) is seen to be a feasible program to address the negative environmental externalities through Information and Communication Technologies (ICTs), but it lacks the quantitative evidence so far. This study aims to examine the impacts and potential mechanisms of SCI on air pollution governance from the objective satellite monitoring data within a quasi-natural experiment framework. We find that SCI directly reduces the air pollutants concentration such as PM2.5, SO2, NO2, and smog in urban China and improves the air quality very well, which also has significant and positive spillovers on air pollution governance in adjacent cities. This encouraging phenomenon can also be achieved through contributing to green technological innovation, industrial structure upgrading, and decentralizing urban spatial structure, such that most of can be attributed to the technological effect. Heterogeneity analyses demonstrate that the governance effect of air pollution is more obvious in large smart cities, and increases with the expansion of city size. Additionally, the effect performs better in resource-based smart cities and smart cities with stronger financing capacity and air pollution pressure.
China has made progress in energy transition to improve air quality, but still confronts challenges including further ambient PM2.5 reduction, O3 pollution mitigation, and CO2 emission control. To explore the coordinated effects of energy transition on air quality and carbon emission in the near term in China, we designed 4 scenarios in 2025 based on different projections of energy transition with varying end-of-pipe control level, in each of which we calculated emissions of major air pollutants and CO2, and simulated ambient PM2.5 and O3 concentrations. Results show that energy transition has disparate effects on emission reduction of different air pollutants and sectors, which largely depends on their current end-of-pipe control levels. The different effects on emission reduction may result in opposite variation tendencies of ambient PM2.5 and O3 concentration in a scenario with aggressive energy transition policies and end-of-pipe control level in 2018. With the end-of-pipe control level strengthened in 2025, PM2.5 and O3 concentration could both reduce on the national scale, but the reduction of ambient O3 lags behind PM2.5, indicating the difficulty of O3 pollution control. As to CO2, national emission would go up in 2025 either implementing current or aggressive energy transition policies due to growing needs of electricity and on-road transportation, but emissions in most provinces could decline to below the 2018 level with aggressive energy transition policies because of substitution of clean energy in industrial, residential and off-road transportation sectors. The study results suggest strictly implementing restrictive end-of-pipe control measures along with energy transition to simultaneously reduce ambient PM2.5 and O3 concentration, and accelerating substitution of renewable energy in power sectors where electricity generation grows rapidly to synergistically control air pollution and CO2 emission. Furthermore, the projection of CO2 emissions could provide references for short-term emission control targets from the perspective of air quality improvement.
The atmospheric nitrogen deposition plays a crucial role in natural ecosystem, and the changes in emissions substantially affect the amount of nitrogen deposition. Along with the decrease in NOx emissions and increase in NH3 emissions, the reduced nitrogen deposition may play a more important role in future. However, to what extent these changes may modify the reduced nitrogen deposition across East Asia, which is fulfilled with a large amount of nitrogen deposition, to the northwestern Pacific has not yet to be clear. Based on the results of multi-model ensemble of Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP), the future changes of reduced nitrogen (NHx) deposition is firstly examined. Here we show the changes of NHx deposition flux is substantially modulated by both climate change and emissions, exhibiting an increasing trend over East Asia-Northwest Pacific in future under representative concentration pathways (RCP) 8.5 scenario, largely controlled by increase of NH3 emissions, contrasting to the oxidized nitrogen deposition which is projected to decrease. Specifically, the ratio of NHx to total nitrogen deposition in eastern China increases from 38% at present to 56% by the end of the century under RCP 8.5, indicative of a transition in the form of dominant nitrogen deposition from oxidized to reduced one. The increase is clearly discernable over the marginal seas and northwestern Pacific. Moreover, we identify a meridional shift of high wet NHx deposition from northern China in summer to southern China in the other seasons. Based on simulations from regional model Weather Research and Forecasting (WRF) and Community Multi-scale Air Quality (CMAQ) model, we find that the synergistically nonlinear modulation of NHx concentration and precipitation triggers the north-south shift of wet NHx deposition. The findings in this study indicate a potentially more important role of reduced nitrogen deposition on the natural ecosystem in future.
A thermal internal boundary layer (TIBL) can cause the fumigation effect and reduce the dispersion capacity of air pollutants. The paper's goal is to quantify the interactions of pollutants between cities subjected to enhanced fumigation effects. To that end, an observational campaign was launched at the Shandong Peninsula of China in the winter of 2020. The campaign focused on monitoring vertical profiles of aerosols, wind field, temperature, and humidity for the boundary-layer dynamic-thermodynamic structure. The Nested Air Quality Prediction Modeling System (NAQPMS) coupled with online source-tagging model was also employed to delve into the source-receptor relationships of the cities involved in the fumigation effect. The results indicated that the presence of a TIBL structure triggered the prolonged and widespread pollution episodes. This resulted in a temperature inversion (250–1000 m in altitude) near the convergence line on surface and rendered the atmospheric boundary layer more stably stratified, i.e., a situation that was unfavorable for the diffusion of pollutants against a persistently sinking flow (with a mean velocity of roughly 0.2 m s⁻¹) for a prolonged time. Meanwhile, TIBLs prevented the fumigation areas from interacting with adjacent inland areas (Shandong Peninsula's local contribution accounting for >90% of the PM2.5) and facilitated inter-city transport of pollutants inside this peninsula (the transport contribution between cities reached up to 55–75%). Such distinct behaviors as observed over this coastal area render air pollution control more of an elusive issue, as is referenced to several inland cities.
Monitoring and predicting the occurrence and dynamic distributions of emerging contaminants (ECs) in the aquatic environment has always been a great challenge. This study aims to explore the potential of fully utilizing the advantages of combining traditional process-based models (PBMs) and data-driven models (DDMs) with general water quality indicators in terms of improving the accuracy and efficiency of predicting ECs in aquatic ecosystems. Two representative ECs, namely Bisphenol A (BPA) and N, N-diethyltoluamide (DEET), in a tropical reservoir were chosen for this study. A total of 36 DDMs based on different input datasets using Artificial Neural Networks (ANN) and Random Forests (RF) were examined in three case studies. The models were applied in prognosis validation based on easily accessible data on water quality indicators. Our results revealed that all the models yielded good fits when compared to the observed data. These new insights into the advantages using the combination of traditional PBMs and DDMs with general water quality datasets help to overcome the constraints in terms of model accuracy and efficiency as well as technical and budget limitations due to monitoring surveys and laboratory experiments in the study of fate and transport of ECs in aquatic environments.
Air pollution has seriously endangered human health and the natural ecosystem during the last decades. Air quality monitoring stations (AQMS) have played a critical role in providing valuable data sets for recording regional air pollutants. The spatial representativeness of AQMS is a critical parameter when choosing the location of stations and assessing effects on the population to long-term exposure to air pollution. In this paper, we proposed a methodological framework for assessing the spatial representativeness of the regional air quality monitoring network and applied it to ground-based PM2.5 observation in the mainland of China. Weighted multidimensional Euclidean distance between each pixel and the stations was used to determine the representativeness of the existing monitoring network. In addition, the K-means clustering method was adopted to improve the spatial representativeness of the existing AQMS. The results showed that there were obvious differences among the representative area of 1820 stations in the mainland of China. The monitoring stations could well represent the PM2.5 spatial distribution of the entire region, and the effectively represented area (i.e. the area where the Euclidean distance between the pixels and the stations was lower than the average value) accounted for 67.32% of the total area and covered 93.12% of the population. Forty additional stations were identified in the Northwest, North China, and Northeast regions, which could improve the spatial representativeness by 14.31%.
soil heavy metals pollution has been becoming one of the severely environmental issues globally. Previous studies reported laboratory-measured spectra could be used to infer soil heavy metals concentrations to some extent. However, using field-obtained spectra to estimate soil heavy metals concentrations is still a great challenge due to the low precision and weak efficiency at large scales. The present study collected 110 topsoil samples from an Opencast Coal Mine of Ordos, Inner Mongolia, China. Then, the spectra and soil heavy metals concentrations of samples were measured under laboratory conditions. The direct standardization (DS) algorithm was introduced to calibrate the Gaofen-5 (GF-5) hyperspectral image based on the measured spectra of samples. The spectral reflectance of the GF-5 hyperspectral image was reconstructed using continuous wavelet transform (CWT) at different scales. The characteristic bands of GF-5 for estimating heavy metals concentrations were selected by the Boruta algorithm. Finally, the random forest (RF), the extreme learning machine (ELM), the support vector machine (SVM), and the back-propagation neural network (BPNN) algorithms were used to predict the heavy metals concentrations. Some findings were achieved. First, CWT can effectively eliminate the noise of satellite hyperspectral data. The characteristic bands of Zn (480–677, 827–1029, 1241–1334, 1435–1797, and 1949–2500 nm), Ni (514–630, 835–985, 1258–1325, 1460–1578, and 1949–2319 nm), and Cu (822–831; 1029–1300, 1486–1595, and 1730–2294 nm) can be effectively retrieved via the Boruta algorithm. Second, the estimation accuracy was significantly improved by using the DS algorithm. For zinc (Zn), nickel (Ni), and copper (Cu), the determination coefficients of the validation dataset (Rv2) were 0.77 (RF), 0.62 (RF), and 0.56 (ELM), respectively. Third, the distribution trends of heavy metals were almost consistent with the results of actual ground measurements. This paper revealed that the GF-5 can be one of the reliable satellite hyperspectral imagery for mapping soil heavy metals.
Background
Urban environment noise has been linked with wide adverse effects on health; however, noise epidemiological researches are hindered by the lack of large-scale population-based exposure assessment.
Objective
We aimed to measure noise levels over multiple seasons and to establish an LUR model to assess the spatial variability of intra-urban noise and identify its potential sources in Shanghai, China.
Methods
Forty-minute (LAeq, 40 min) measurements of environmental noise were collected at 144 fixed sites, and each was visited three times (morning, afternoon, and evening) in winter, spring, and summer in 2019. Noise measurements were then integrated with land-use types, road networks, socioeconomic variables, and geographic information systems to construct LUR models. Ten-fold cross-validation was used to test the model performance.
Results
A total of 1296 measurements and 29 predicting variables were used to estimate the spatial variation in environmental noise. The annual mean (±standard deviation) of LAeq, 40min, was 62 ± 8 dB (A). Significant variations were observed among monitoring sites but not between seasons or time of day. The LUR model explained 79% of the spatial variability of the noise, and the R² of the ten-fold cross-validation was 0.75. The most contributory predictors of noise level were road-related variables all within the 50-m buffers, followed by urban area within a 50-m buffer, total area of buildings within a 1000-m buffer, and number of restaurant clusters within a 50-m buffer. Farmland area within a 100-m buffer was the only negative variable in the model. A 50-m resolution noise prediction map was produced and suggested high noise level in urban areas and near traffic arteries.
Conclusion
LUR can be a robust method for reflecting noise variability in megacities such as Shanghai and may provide an efficient solution for noise exposure assessment in areas where noise maps are not available.
In recent years, winter PM2.5 and summer O3 pollution which often occurred with air stagnation condition has become a major concern in China. Thus, it is imperative to understand the air stagnation distribution in China and elucidate its impact on air pollution. In this study, three air stagnation indices were calculated according to atmospheric thermal and dynamics parameters using ERA5 data. Two improved indices were more suitable in China, and they displayed similar characteristics: most of the air stagnant days were found in winter, and seasonal distributions showed substantial regional heterogeneity. During stagnation events, flat west or northwest winds at 500 hPa and high pressure at surface dominated, with high relative humidity (RH) and temperature (T), weak winds in most regions. The pollutants concentrations on stagnant days were higher than those on non-stagnant days in most studied areas, with the largest difference of the 90th percentiles of maximum daily 8-h average (MDA8) O3 up to 62.2 μg m⁻³ in Pearl River Delta (PRD) and PM2.5 up to 95.8 μg m⁻³ in North China Plain (NCP). During the evolution of stagnation events, the MDA8 O3 concentrations showed a significant increase (6.0 μg m⁻³ day⁻¹) in PRD and a slight rise in other regions; the PM2.5 concentrations and the frequency of extreme PM2.5 days increased, especially in NCP. Furthermore, O3 was simultaneously controlled by temperature and stagnation except for Xinjiang (XJ), with the average growth rate of 19.5 μg m⁻³ every 3 °C at 19 °C–31 °C. PM2.5 was dominated by RH and stagnation in northern China while mainly controlled by stagnation in southern China. Notably, the extremes of summer O3 (winter PM2.5) pollution was most associated with air stagnation and T at 25 °C–31 °C (air stagnation and RH >50%). The results are expected to provide important reference information for air pollution control in China.
Ozone (O3) is an important trace and greenhouse gas in the atmosphere, posing a threat to the ecological environment and human health at the ground level. Large-scale and long-term studies of O3 pollution in China are few due to highly limited direct ground and satellite measurements. This study offers a new perspective to estimate ground-level O3 from solar radiation intensity and surface temperature by employing an extended ensemble learning of the space-time extremely randomized trees (STET) model, together with ground-based observations, remote sensing products, atmospheric reanalysis, and an emission inventory. A full-coverage (100%), high-resolution (10 km) and high-quality daily maximum 8-hour average (MDA8) ground-level O3 dataset covering China (called ChinaHighO3) from 2013 to 2020 was generated. Our MDA8 O3 estimates (predictions) are reliable, with an average out-of-sample (out-of-station) coefficient of determination of 0.87 (0.80) and root-mean-square error of 17.10 (21.10) µg/m 3 in China. The unique advantage of the full coverage of our dataset allowed us to accurately capture a short-term severe O3 pollution exposure event that took place from 23 April to 8 May in 2020. Also, a rapid increase and recovery of O3 concentrations associated with variations in anthropogenic emissions were seen during and after the COVID-19 lockdown, respectively. Trends in O3 concentration showed an average growth rate of 2.49 μg/m 3 /yr (p < 0.001) from 2013 to 2020, along with the continuous expansion of polluted areas exceeding the daily O3 standard (i.e., MDA8 O3 = 160 µg/m 3). Summertime O3 concentrations and the probability of occurrence of daily O3 pollution have significantly increased since 2015, especially in the North China Plain and the main air pollution transmission belt (i.e., the "2+26" cities). However, a decline in both was seen in 2020, mainly due to the coordinated control of air pollution and ongoing COVID-19 effects. This carefully vetted and smoothed dataset is valuable for studies on air pollution and environmental health in China.
Air pollution is a significant urban issue, with practical applications for pollution control, urban environmental management planning, and urban construction. However, owing to the complexity and differences in spatiotemporal changes for various types of pollution, it is challenging to establish a framework that can capture the spatiotemporal correlations of different types of air pollution and obtain high prediction accuracy. In this paper, we proposed a deep learning framework suitable for predicting various air pollutants: a graph convolutional temporal sliding long short-term memory (GT-LSTM) model. The hybrid integrated model combines graph convolutional networks and long short-term networks based on a strategy with temporal sliding. Herein, the graph convolution networks gather neighbor information for spatial dependency modeling based on the spatial adjacency matrices of different pollutants and the graph convolution operator with parameter sharing. LSTM networks with a temporal sliding strategy are used to learn dynamic air pollution changes for temporal dependency modeling. The framework was applied to predict the average concentrations of PM2.5, PM10, O3, CO, SO2, and NO2 in the Bejing-Tianjin-Hebei (BTH) region for the next 24 hours. Experiments demonstrated that the proposed GT-LSTM model could extract high-level spatiotemporal features and achieve higher accuracy and stability than state-of-the-art baselines. Advancement in this methodology can assist in providing decision support capabilities to mitigate air quality issues.
Emission regulations of the power and industry sectors have been identified as the major driver of PM2.5 mitigation over China during 2013-2017. In this study, we use ground-based observations of four air pollutants (CO, NO2, SO2, and PM2.5) to show that additional stringent emission policies on the industrial, transportation, and residential sectors during the new 3-year protection plan (2018-2020) have accelerated the improvement of China's air quality.
Based on regional (North and South China) trends of annual mean measurements, significant reductions are observed for all four pollutants during 2017-2020. These decreasing trends are found to be >30% stronger than 2015-2017 for NO2, CO, and PM2.5. For CO and PM2.5, the acceleration is the strongest in winter and North China, when and where the residential clean-heating actions were implemented. While for NO2, the accelerations are pronounced regardless of region or season, reflecting nationwide measures to reduce NOx emissions from industrial and transportation activities. SO2 concentration reductions that were already substantial before 2017 are maintained but not accelerated, consistent with the dominance of end-of-pipe measures rather than a structural change of energy fuels. Our investigation highlights the value of multi-pollutant analysis to relate emission policies with air quality changes.
Industrialization and increasing urbanization have led to increased air pollution, which has a devastating effect on public health and asthma. This study aimed to model the spatial-temporal of asthma in Tehran, Iran using a machine learning model. Initially, a spatial database was created consisting of 872 locations of asthma children and six air pollution parameters, including carbon monoxide (CO), particulate matter (PM10 and PM2.5), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O3) in four-seasons (spring, summer, autumn, and winter). Spatial-temporal modeling and mapping of asthma-prone areas were performed using a random forest (RF) model. For Spatio-temporal modeling and assessment, 70 % and 30 % of the dataset were used, respectively. The Spearman correlation and RF model findings showed that during different seasons, the PM2.5 parameter had the most important effect on asthma occurrence in Tehran. The assessment of the Spatio-temporal modeling of asthma using the receiver operating characteristic (ROC)-area under the curve (AUC) showed an accuracy of 0.823, 0.821, 0.83, and 0.827, respectively for spring, summer, autumn, and winter. According to the results, asthma occurs more often in autumn than in other seasons.
While temperature rises in urbanized area there is a growing concern among key decision-makers and urban planners to actively incorporate Urban Heat Island (UHI)-related considerations in their development/design. However, given that the existing models (mainly physics-based) are too complex to use, there is a need for an easy-to-use decision support tool that provides an explicit understanding of the contributions of different urban planning decision-making parameters on UHI. To this end, this research uses publicly available data to develop a data-driven methodology that mines explicit rules about the correlation between socio-economic and urban morphology features and UHI at a street-level. By implementing a tree-regression approach, five distinct categories of potential UHI were identified. These categories represent five levels of UHI, from low to high, where explicit thresholds are identified for each feature. The optimal model based on accuracy and interpretability is a decision tree (DT), with an accuracy of 93 %. With the results of the case study, it is demonstrated that (1) the proposed methodology leads to an easy-to-use tool that can be implemented by urban planners to investigate the impact of their design choices at the street-level, and (2) the results obtained are consistent with the current body of knowledge, which in turn alleviates the drawbacks of traditional methods.
To evaluate the evolution of river water quality in a changing environment, measuring the objective water quality is critical for understanding the rules of river water pollution. Based on the sample entropy theory and a nonlinear statistical method, this study aims to identify the spatiotemporal dynamics of water quality and its complexity in the Yangtze River basin using time series data, to separate the contributions of human activity and climate change to water quality, and to establish a data-driven risk assessment framework for the spatial (potential risk) and temporal (direct risk) aspects of water pollution. The results demonstrate that the spatiotemporal dynamics of water quality and sample entropy in each monitoring section are closely related to the characteristics of the corresponding location. The water quality of the main stream is superior, and its complexity is less than that of the tributaries. Cascade reservoir operation and vegetation status, agricultural production, and rainfall patterns exert great influences in the upper, middle, and lower reaches, respectively. Dam construction, urban agglomeration development, and interactions between river and lake are also influencing factors. An attributional analysis found that climate change and human activities negatively contributed to the evolution of NH3-N concentration in most of the monitored sections, and the average relative contribution rates of human activities to changes in water quality in the main and tributary streams were − 55.46% and − 48.49%, respectively. In addition, the construction of data-driven risk assessment framework can efficiently and accurately assess the potential and direct water pollution risks of rivers.
Analyzing the distribution of air pollution and its influencing factors is critical for regional air pollution prevention and control. This study takes the Pearl River Delta (PRD) as a study area, analyzes the spatial-temporal changes in air pollution (including sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), carbon monoxide (CO), and particles with an aerodynamic diameter less than 10 μm (PM10) and less than 2.5 μm (PM2.5)) from 2006 to 2019, and explores the relationship between air pollution and meteorological factors. The results showed that (1) most air pollutant concentrations decreased from 2006 to 2019, with the most obvious change being the decrease in the SO2 concentration from 52.4 μg/m³ to 7.8 μg/m³. The O3 concentration increased, with a Sen's slope of 0.649 μg·m⁻³·year⁻¹. Air pollution was lower in coastal areas (Shenzhen and Huizhou) than in inland areas (Foshan and Zhaoqing), affected by ocean atmospheric transmission, and coastal areas had the cleanest air quality. (2) Air pollutants had high correlations with air pressure, relative humidity, precipitation, and temperature. The most serious air pollution was found in winter, which was partially due to the meteorological conditions in winter that were more unfavorable for pollutant dispersion and dilution than were the conditions in other seasons. (3) Through the wavelet coherence method, an interesting finding revealed that other air pollution and meteorological factors exhibited complex period-dependent characteristics that were significantly related to PM2.5. Areas with less air pollution were more susceptible to meteorological factors. (4) The overlapping area of the PM2.5 distribution hotspot and nighttime light hotspot was mainly in FS and GZ, representing approximately 12.1% of the study area. Our work contributes to the literature by considering seasonal and timescale-dependent characteristics of meteorological factors affecting air pollutant emissions, and it provides new insights into recognizing regions that need to prioritize urban air pollution control based on hotspot analysis.
Since 2018, the Blue Sky Protection Campaign (BSPC) have been implemented at unprecedented levels to combat air pollution in the Beijing-Tianjin-Hebei (JJJ) region. In this study, the GAINS IV Asia (Greenhouse Gas and Air Pollution Interactions and Synergies) model is used to assess the potential for air pollution abatement, air quality improvement and associated costs of the BSPC in the JJJ region. The key findings are: 1) The total energy consumption under BSPC will decrease by 3%, 2%, and 6% for Beijing, Tianjin, and Hebei, respectively, by 2020 compared with the baseline scenario and Hebei is projected to experience the greatest changes in energy consumption both in absolute terms and in proportion. 2) Hebei would have the largest air pollution abatement. Compared to 2015, emissions of NOx, PM2.5, SO2, and NH3 under 2020 will be decrease by 31.3% 44.8%, 40.3%, and 10.7%, respectively. Residential and industrial combustion play vital contributions for pollution abatement, accounting for 52.3% together of Hebei’s total. 3) Emissions of NOx, PM2.5, and SO2 of Beijing will decrease by 21.2%, 58.7%, and 56.1% by 2020, compared to 2015. Transportation and residential sectors have key contributions to reductions. 4) The population-weighted annual PM2.5 concentration would decrease to 41.4μg/m³, 49.4μg/m³, and 53.8μg/m³ by 2020 in Beijing, Tianjin, and Hebei, respectively. Finally, we recommend that actions of increasing nitrogen use efficiency of agricultural sector, super low emission standard in industrial sector, and switching off coal power plants are most cost-effective in air quality improvement during 2020-2025.
Nitrous oxide, N2O, is the leading cause of stratospheric ozone depletion and one of the most potent greenhouse gases (GHG). Its concentration in the atmosphere has been rapidly increasing since the green revolution in the 1950s and 1960s. Riverine systems have been suggested to be an important source of N2O, although their quantitative contribution has been estimated with poor precision, ranging between 32.2 and 2,100 GgN2O − N/yr. Here, we quantify reach scale N2O emissions by integrating a data-driven machine learning model with a physically-based upscaling model. The application of this hybrid modeling approach reveals that small streams (those with widths less than 10 m) are the primary sources of riverine N2O emissions to the atmosphere. They contribute nearly 36 GgN2O − N/yr; almost 50% of the entire N2O emissions from riverine systems, although they account for only 13% of the total riverine surface area worldwide. Large rivers (widths wider than 175 m), such as the main stems of the Amazon River (∼ 6 GgN2O − N/yr), the Mississippi River (∼ 2 GgN2O − N/yr), the Congo River (∼ 1 GgN2O − N/yr) and the Yang Tze River (∼ 0.7 GgN2O − N/yr), only contribute 26% of global N2O emissions, which primarily originate from their water column. This study identifies, for the first time, near-global N2O emission hot spots within watersheds and thus can aid the development of local- to global-scale management and mitigation strategies for riverine systems with respect to N2O emissions.
https://authors.elsevier.com/a/1cf0t_17GgI59s
The homoscedasticity assumption (the variance of the error term is the same across all the observations) is a key assumption in the ordinary linear squares (OLS) solution of a linear regression model. The validity of this assumption is examined for a multiple linear regression model used to determine the source contributions to the observed black carbon concentrations at 12 background monitoring sites across China using a hybrid modeling approach. Residual analysis from the traditional OLS method, which assumes that the error term is additive and normally distributed with a mean of zero, shows pronounced heteroscedasticity based on the Breusch–Pagan test for 11 datasets. Noticing that the atmospheric black carbon data are log-normally distributed, we make a new assumption that the error terms are multiplicative and log-normally distributed. When the coefficients of the multilinear regression model are determined using the maximum likelihood estimation (MLE), the distribution of the residuals in 8 out of the 12 datasets is in good accordance with the revised assumption. Furthermore, the MLE computation under this novel assumption could be proved mathematically identical to minimizing a log-scale objective function, which considerably reduces the complexity in the MLE calculation.
The new method is further demonstrated to have clear advantages in numerical simulation experiments of a 5-variable multiple linear regression model using synthesized data with prescribed coefficients and lognormally distributed multiplicative errors. Under all 9 simulation scenarios, the new method yields the most accurate estimations of the regression coefficients and has significantly higher coverage probability (on average, 95% for all five coefficients) than OLS (79%) and weighted least squares (WLS, 72%) methods.
To improve air quality, China formulated the Air Pollution Prevention and Control Action Plan (APPCAP) in 2013. In the present study, the changes in the concentration of air pollutants after the implementation of APPCAP were investigated based on nationwide monitoring data. From the results, it is evident that the annual mean concentrations of PM2.5, PM10, SO2, and CO show a significant downward trend over 2015-2018, with decreasing rates of 3.4, 4.1, 3.8, and 70 μg·m⁻³/year, respectively. However, no significant change was found in NO2 while maximum daily 8h average O3 concentration (MDA8 O3) was increased by 3.4 μg·m⁻³/year during the four years. Spatially, the highest decrease in PM2.5 was found in Beijing-Tianjin-Hebei (BTH), followed by central China and northeast China, while the Pearl River Delta (PRD), Yungui Plateau, and northwest China showed less decreases. MDA8 O3 had a higher increase in BTH, central China, Yangtze River Delta (YRD), and PRD. With the decrease in PM2.5 in recent years, cumulative population exposure to PM2.5 gradually decreased, whereas there was still more than 65% of the population exposing to annual PM2.5 higher than the standard of 35 μg·m⁻³ in 2018. In contrast, the health effects of O3 gradually increased with 13.1%, 14.3%, 20.4%, and 21.7% of the population exposed to unhealthy O3 levels in summer from 2015 to 2018. O3 pollution is causing severe health risks with estimated nationwide mortality of 70,024 (95% CI: 55,510-84,501), 79,159 (95% CI: 62,750-95,525), 105,150 (95% CI: 83,378-126,852), and 104,404 (95% CI: 82,784-125,956) in the four years, respectively. This clearly shows that the target of air pollution control in China shifts and coordinated control of PM2.5 and O3 is urgently needed after the successful implementation of APPCAP.
The lack of long-term observations and satellite retrievals of health-damaging fine particulate matter in China has demanded the estimates of historical PM2.5 (particulate matter less than 2.5 μm in diameter) concentrations. This study constructs a gridded near-surface PM2.5 concentration dataset across China covering 1980–2019 using the space-time random forest model with atmospheric visibility observations and other auxiliary data. The modeled daily PM2.5 concentrations are in excellent agreement with ground measurements, with a coefficient of determination of 0.95 and mean relative error of 12%. Besides the atmospheric visibility which explains 30% of total importance of variables in the model, emissions and meteorological conditions are also key factors affecting PM2.5 predictions. From 1980 to 2014, the model-predicted PM2.5 concentrations increased constantly with the maximum growth rate of 5–10 μg/m³/decade over eastern China. Due to the clean air actions, PM2.5 concentrations have decreased effectively at a rate over 50 μg/m³/decade in the North China Plain and 20–50 μg/m³/decade over many regions of China during 2014–2019. The newly generated dataset of 1-degree gridded PM2.5 concentrations for the past 40 years across China provides a useful means for investigating interannual and decadal environmental and climate impacts related to aerosols.
Numerous methods have been implemented to evaluate the relationship between environmental factors and respiratory mortality. However, the previous epidemiological studies seldom considered the spatial and temporal variation of the independent variables. The present study aims to detect the relations between respiratory mortality and related affecting factors across Xi'an during 2014–2016 based on a novel geographically and temporally weighted regression model (GTWR). Meanwhile, the ordinary least square (OLS) and the geographically weighted regression (GWR) models were developed for cross-comparison. Additionally, the spatial autocorrelation and Hot Spot analysis methods were conducted to detect the spatiotemporal dynamic of respiratory mortality. Some important outcomes were obtained. Socioeconomic and environmental determinants represented significant effects on respiratory diseases. The respiratory mortality exhibited an obvious spatial correlation feature, and the respiratory diseases tend to occur in winter and rural areas of the study area. The GTWR model outperformed OLS and GWR for determining the relations between respiratory mortality and socioeconomic as well as environmental determinants. The influence degree of anthropic factors on COPD mortality was higher than natural factors, and the effects of independent variables on COPD varied timely and locally. The results can supply a scientific basis for respiratory disease controlling and health facilities planning.
A health impact assessment of the indoor pollution was performed for various indoor sources: oven for heating, cooking, photocopy machine and smoke cigarettes. The mortality levels and hospital admissions associated with exposure to PM2.5 and NO2 concentrations have been calculated. We have modelled a two level house in Madrid city center where the office and the living floors are in the same building. The people follow a predefined activity patterns (time profiles) in the outdoor and indoor environments. In this experiment, we have performed a full year simulation using the EnergyPlus model to obtain the following parameters: building energy use, thermal behavior, airflow and indoor air quality simultaneously. Outdoor air quality and meteorological conditions were provided by the output of running the very well-known model WRF/Chem. The health impacts of the indoor emitting sources are higher in the warm months due to the operation of the air conditioning system. The largest impact on health is produced by the emissions that are released during cooking. The results also show a high correlation between indoor and outdoor concentrations when indoor emissions are not considered.
Exposure to fine particulate matter (PM2.5) can significantly harm human health and increase the risk of death. Satellite remote sensing allows for generating spatially continuous PM2.5 data, but current datasets have overall low accuracies with coarse spatial resolutions limited by data sources and models. Air pollution levels in China have experienced dramatic changes over the past couple of decades. However, country-wide ground-based PM2.5 records only date back to 2013. To reveal the spatiotemporal variations of PM2.5, long-term and high-spatial-resolution aerosol optical depths, generated by the Moderate Resolution Imaging Spectroradiometer (MODIS) Multi-Angle implementation of Atmospheric Correction (MAIAC) algorithm, were employed to estimate PM2.5 concentrations at a 1-km resolution using our proposed Space-Time Extra-Trees (STET) model. Our model can capture well variations in PM2.5 concentrations at different spatiotemporal scales, with higher accuracies (i.e., cross-validation coefficient of determination, CV-R2 = 0.86–0.90) and stronger predictive powers (i.e., R2 = 0.80–0.82) than previously reported. The resulting PM2.5 dataset for China (i.e., ChinaHighPM2.5) provides the longest record (2000 to 2018) at a high spatial resolution of 1 km, enabling the study of PM2.5 variation patterns at different scales. In most places, PM2.5 concentrations showed increasing trends around 2007 and remained high until 2013, after which they declined substantially, thanks to a series of government actions combating air pollution in China. While nationwide PM2.5 concentrations have decreased by 0.89 μg/m3/yr (p < 0.001) during the last two decades, the reduction has accelerated to 4.08 μg/m3/yr (p < 0.001) over the last six years, indicating a significant improvement in air quality. Large improvements occurred in the Pearl and Yangtze River Deltas, while the most polluted region remained the North China Plain, especially in winter. The ChinaHighPM2.5 dataset will enable more insightful analyses regarding the causes and attribution of pollution over medium- or small-scale areas.