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The four pollution weather types as a function of their (a) number of samples, (b) proportion with respect to the total number of samples, and (c) interpretation variance.
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Different types of pollution boundary layer structures form via the coupling of different synoptic systems and local mesoscale circulation in the boundary layer; this coupling contributes toward the formation and continuation of haze pollution. In this study, we objectively classify the 32 heavy haze pollution events using integrated meteorological...
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Context 1
... is, influenced by easterly winds at the bottom of the high-pressure system; (c) type 3, that is, a weak downdraft effect in the high-pressure system; and (d) type 4: no significant weather system. In this study, we observed 125 d of heavy polluted weather. Among these days, type 1, type 2, type 3, and type 4 had 67, 27, 21, and 10 d, respectively (Fig. 2), where the four weather types accounted for 53.6 %, 21.6 %, 16.8 %, and 8.0 % of the total sampled weather event days, respectively. The total interpretation variance of the four types for all events was 97.8 %, while the independent interpretation variance was 43.69 %, 33.68 %, 16.51 %, and 3.92 %, respectively (Fig. 2). This ...
Context 2
... and 10 d, respectively (Fig. 2), where the four weather types accounted for 53.6 %, 21.6 %, 16.8 %, and 8.0 % of the total sampled weather event days, respectively. The total interpretation variance of the four types for all events was 97.8 %, while the independent interpretation variance was 43.69 %, 33.68 %, 16.51 %, and 3.92 %, respectively (Fig. 2). This indicates that an objective weather classification can effectively obtain the main feature information of the pollution weather ...
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... Meteorological factors, including temperature, humidity, wind speed and rainfall, play a vital role in influencing atmospheric pollutants. Understanding and monitoring these factors are crucial for effectively controlling pollution levels in urban areas (Giri et al. 2008;Guo et al. 2022;Lin et al. 2020;Sun et al. 2021;Wang et al. 2014). Evidence from numerous research studies indicates that meteorological conditions can indeed alter air quality (Birinci et al. 2023;Bose and Roy Chowdhury 2023;Cai et al. 2023;Castelhano et al. 2022). ...
Meteorological conditions significantly impact ambient air quality in urban environments. This study focuses on Asansol, known as the “Coal City” and the “Industrial Heart of West Bengal,” a notable hotspot for air pollution. Despite its significance, limited research has addressed the influence of meteorological factors on key air pollutants in this urban area. From January 2019 to December 2023, this investigation explores the relationships between meteorological parameters (including atmospheric temperature, relative humidity, rainfall, wind speed) and the concentrations of crucial air pollutants (PM2.5, PM10, NO2, SO2). Temporal trends in air pollutant concentrations are also analysed. The Spearman correlation method is used to establish associations between pollutant concentrations and meteorological variables, while multiple linear regression (MLR) models are employed to assess meteorological factors and potential impact on pollutant concentrations. The analysis reveals a decreasing trend in pollutant concentrations in Asansol. Temperature exhibits negative correlations with all pollutants in all seasons except for a positive correlation during the monsoon. Rainfall consistently displays significant negative correlations with pollutants in all seasons. Relative humidity is negatively correlated with pollutants in all seasons, and wind speed, except during the post-monsoon season, shows negative correlations with all pollutants. Linear models excel in predicting particulate matter concentrations but perform poorly in predicting gaseous contaminants. Accounting for seasonal fluctuations and meteorological parameters, this research enhances the accuracy of air pollution forecasting, contributing to a better understanding of air quality dynamics in Asansol and similar urban areas.
... In winter, the NO 2 concentrations exhibit relatively stable diurnal variations. With lower atmospheric boundary layers and the frequent occurrence of temperature inversions in Xianghe during winter, NO 2 tends to accumulate in the lower atmosphere, persisting for longer periods within the troposphere [58][59][60]. ...
This study analyzed the differences in ozone (O3) sensitivity in four different urban areas in China from February 2019 to January 2020 based on data on various near-surface pollutants from passive multi-axis differential optical absorption spectroscopy (MAX-DOAS) sites and nearby China National Environmental Monitoring Center (CNEMC) sites. Across the four cities, the nitrogen dioxide (NO2) and formaldehyde (HCHO) concentrations varied seasonally. Xianghe consistently displayed the lowest NO2 levels, suggesting reduced emissions compared to other cities. Guangzhou, a city with a robust economy and a high level of vehicle ownership, exhibited higher concentrations in spring. Summer brought elevated HCHO levels in Guangzhou, Xianghe, and Shenyang due to intensified photochemical processes. Autumn and winter showed higher HCHO concentrations in Guangzhou and Xianghe compared to Lanzhou and Shenyang. Overall, Guangzhou recorded the highest annual averages, due to its developed economy, while Xianghe’s lower NO2 levels were offset by the elevated HCHO due to higher O3 values. The analysis delved into primary and secondary HCHO sources across seasons and used carbon monoxide (CO) and O3 data. Xianghe showcased the dominance of secondary sources in summer and autumn, while Lanzhou was characterized by primary dominance throughout the year. Shenyang mirrored Xianghe’s evolution due to industrial emissions. In Guangzhou, due to the high levels of vehicular traffic and sunlight conditions, secondary sources predominantly influenced HCHO concentrations. These findings highlight the interplay between primary and secondary emissions in diverse urban settings. This study explored O3 sensitivity variations across seasons. Xianghe exhibited a balanced distribution among volatile organic compound (VOC)-limited conditions, nitrogen oxide (NOx)-limited conditions, and transitional influences. Lanzhou was mainly affected by VOC-limited conditions in winter and NOx-limited conditions in other seasons. Shenyang’s sensitivity varied with the seasons and was primarily influenced by transitions between VOCs and NOx in autumn and NOx-limited conditions otherwise. Guangzhou experienced varied influences. During periods of high O3 pollution, all regions were affected by NOx-limited conditions, indicating the necessity of NOx monitoring in these areas, especially during summer in all regions and during autumn in Xianghe and Guangzhou.
... Furthermore, the comprehensive understanding of global air pollution dynamics could be enhanced by investigating the potential combination of satellite data and widely accessible ground-based sensor networks in public areas. Recent research, such as the study "Boundary layer structure characteristics under the objective classification of persistent pollution weather types in the Beijing area" [23], sheds light on the importance of classifying persistent pollution weather types. This classification provides a framework for objectively characterizing atmospheric conditions, including the structure of the pollution boundary layer. ...
As the world grapples with the pressing challenge of environmental sustainability, the need for innovative solutions to combat air pollution has become paramount. Air pollution is a complex issue that necessitates real-time monitoring of pollution sources for effective mitigation. This paper explores the potential of swarm algorithms applied as a novel and efficient approach to address this critical environmental concern. Swarm algorithms offer a promising framework for coordinating fleets of drones to collaboratively monitor and analyze air pollution sources. The unique capabilities of drones, including their agility, accessibility, and versatility, make them ideal candidates for aerial data collection. When harnessed in a swarm, these drones can create a dynamic and adaptable network that provides a more comprehensive and fine-grained understanding of air pollution dynamics. This paper delves into the conceptual foundations of using swarm algorithms in drone-based air pollution monitoring.
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... Previous studies on ABL classification are usually part of broader works on ABL height detection (Pal et al., 2013;Rieutord et al., 2021;Toledo et al., 2014), parameterization schemes (Harvey et al., 2013;Lock et al., 2000), air quality alerts (Liao et al., 2018;Sun et al., 2021), or cloud characterization (Hogan & O'Connor, 2004;Kotthaus & Grimmond, 2018), for example. Many classifications are made with carefully tuned thresholds and device-dependent decision trees (Hogan & O'Connor, 2004;Kotthaus & Grimmond, 2018;Manninen et al., 2018;Pal et al., 2013). ...
To handle the complexity of the atmospheric boundary layer (ABL) and make accurate feature detection (top height, low‐level jets, inversions, etc.), a prior necessary step is to identify the type of boundary layer. This study proposes a new method to identify the boundary layer type through unsupervised classification and the synergistic use of ground‐based remote sensing. Unsupervised classification is used to lighten the human supervision. The new classification was applied to a 1‐day case study collected during wintertime in the Arve River valley near Chamonix–Mont‐Blanc during the Passy‐2015 field experiment. The ABL classification obtained from microwave radiometer and ceilometer observations (ground‐based remote sensors [GBReS]) combination is compared with high‐frequency radiosoundings (RS) data and the French convective scale AROME model outputs. Classifications from RS and GBReS broadly agree, demonstrating the good behavior of the method, AROME leading to different results at night. The difference of AROME is likely due to the different nature of the data (model fields are smoother and include forecasting errors). The results show the ability of unsupervised classification to segment relevant objects in the boundary layer and the benefit to use a combination of GBReS. Result of unsupervised classification from ceilometer and radiometer data for two to seven clusters. One can see on this example that unsupervised classification shows skill to detect relevant features of the boundary layer. The instrument combination is beneficial to the classification.
... In recent years, under the guidance of the Beijing-Tianjin-Hebei coordinated development strategy, the Beijing Municipal Government has further strengthened the governance of the air quality in the region (Chen et al., 2008;Cao et al., 2015;Miao et al., 2017;Zhai et al., 2019;Gong et al., 2022a;Gong et al., 2022b). It has taken a series of measures such as shutting down high-energy-consuming enterprises, restricting the use of motor vehicles, and widely using new energy (Miao et al., 2015;Su et al., 2015;Su et al., 2015;Ye et al., 2016;Yang et al., 2018;Sun et al., 2021). However, the emission of air pollution precursors from the Yangsha and North China Plain in the central and western regions of Inner Mongolia in autumn and winter still seriously affects the air quality in Beijing, resulting in the frequent occurrence of severe haze events in the region (Miao et al., 2020;Miao et al., 2021;Zhang et al., 2022). ...
... Miao et al., 2020 studied the relationship between severe haze events in the Beijing-Tianjin-Hebei region and the planetary boundary layer and weather circulation and found that under the influence of the southeast high, northern low, and eastern high-pressure systems, warm air often suppresses the development of the boundary layer hight, resulting in the deterioration of air quality in the Beijing-Tianjin-Hebei region. Several studies have shown that local meteorological conditions have a strong dependence on weather circulation, which will affect the specific distribution of pollutants (Levy et al., 2008a;Levy et al., 2008b;Levy et al., 2010;Russo et al., 2016;Ye et al., 2016;Russo et al., 2018;Liu et al., 2019;Xu et al., 2019;Xu et al., 2020;Li X et al., 2021;Sun et al., 2021;Wang et al., 2022a;Wang et al., 2022d). Sun et al., 2021 studied the formation and dissipation mechanism of pollution events and found that the wind speed and temperature in the formation stage of the pollution events were much lower than that in the dissipation stage. ...
... Several studies have shown that local meteorological conditions have a strong dependence on weather circulation, which will affect the specific distribution of pollutants (Levy et al., 2008a;Levy et al., 2008b;Levy et al., 2010;Russo et al., 2016;Ye et al., 2016;Russo et al., 2018;Liu et al., 2019;Xu et al., 2019;Xu et al., 2020;Li X et al., 2021;Sun et al., 2021;Wang et al., 2022a;Wang et al., 2022d). Sun et al., 2021 studied the formation and dissipation mechanism of pollution events and found that the wind speed and temperature in the formation stage of the pollution events were much lower than that in the dissipation stage. Additionally, the changes in pollutant concentrations were significantly affected by changes in relative humidity. ...
The 24th Winter Olympics was held in Beijing, and the air quality in the Beijing area has become the focus of the world’s attention. The Beijing government has taken a series of strict measures to control pollutant emissions during the Winter Olympics, which also provides us with a valuable opportunity to study the impact of meteorological conditions on pollutants. We defined November, December, January, February, and March as the polluted period in Beijing, and used the T-PCA method to divide the circulation types (CTs) affecting Beijing into six kinds (CT1-CT6). It was found that under the control of the western high pressure (CT1) and the northwest high pressure (CT4), the concentrations of PM2.5, NO2, SO2 and CO in Beijing were lower; while under the control of the northern high pressure (CT2), eastern high pressure (CT5), southeast high (CT3) and northeast low pressure (CT6), the concentration of PM2.5, NO2, SO2 and CO is higher. By analyzing the daily CTs, wind field and pollutant concentration changes in the Beijing area during the Beijing Winter Olympics, it was found that when two pollution events occurred during the Winter Olympics, the Beijing area was mainly prevailed by CT2, CT3, and CT6. Comparing the frequency of occurrence of six CTs during the 2022 Winter Olympics and the same period from 2014 to 2021, it was found that the proportion of CT1 and CT4 increased significantly during the Winter Olympics. Finally, the FLEXPART-WRF model was used to analyze the 48-h backward footprint distribution of pollutant particles in Beijing during the Winter Olympics. It further showed that the circulation in the Beijing area during the Winter Olympics was generally conducive to the dispersion of pollutants, and the air quality was better.
... In addition to seriously endangering human health, the PM 2.5 particles also lead to reduced visibility, affect human traffic, normal production, and life. In addition, it affects cloud formation and precipitation and changes in the East Asian monsoon climate system (Yan et al., 2021;Yin et al., 2015;Zhang et al., 2009;Zhou et al., 2019;Han et al., 2015;Ma et al., 2019;Miao et al., 2018;Su et al., 2015;Sun et al., 2021;Jena et al., 2020Jena et al., , 2021. ...
Particulate matter pollution is increasingly serious in Changsha-Zhuzhou-Xiangtan urban agglomeration (CZT) due to rapid industrialization and urbanization. Particulate matter pollution and weather conditions are closely related. In this study, the relationship between the large-scale weather circulation types (CTs), horizontal recirculation wind field, boundary layer structure and particulate matter pollution (PM2.5) in CZT was systematically investigated. Firstly, the Allwine and Whiteman (AW) wind field classification method and Richardson method were used to calculate the horizontal recirculation index and the planetary boundary layer height (PBLH) in CZT. By analyzing the relationship among the recirculation index, the PBLH and the particle concentration, it was found that the recirculation process in CZT could be divided into horizontal recirculation and “pseudo recirculation”. Then, the circulation patterns affecting the CZT were divided into nine types (CT1-CT9) by using T-mode PCA method based on 925 hpa geopotential data. The CZT was prone to recirculation under the control of high pressure rear (CT4), weak high pressure edge (CT5) and southwest vortex (CT7, CT8). It was found that CT4 and CT5 accompanied with lower PBLH and poorer vertical diffusion conditions characterized by high particle concentration. While CT7 and CT8 accompanied with higher PBLH and better vertical diffusion conditions characterized by low particle concentration. Finally, the effect of CTs, recirculation index and boundary layer structure on paticle matter concentration were assessed during two recirculation processes in December 2016 and August 2017, repectively. The analysis was complemented with FLEXPART-WRF model simulations, which confirmed that the recirculation and “pseudo recirculation” can be effectively distinguished by introducing the PBLH, combined with the large-scale weather circulation.
... On the morning of 9 February, influenced by the transpiration effect of sea surface wind and the evaporation effect caused by increasing temperature (Figure 2), an updraft of 2 m/s appeared at the height of 500 m. In fact, the slight vertical turbulence did not disturb the stable atmospheric stratification, but facilitated the collision and growth of smaller particles (liquid drops) into larger scales [57]. As a result, the weak updraft raised the air pollutants, spreading them to the upper layer, and the haze developed into the mature stage. ...
Based on wind profile radar observations, along with high-frequency wave radar data, meteorological data, and air quality monitoring data, we studied a haze episode in Panjin—a coastal city around Bohai Bay in Northeast China—that occurred from 8 to 13 February 2020. The results show that this persistent pollution event was dominated by PM10 and PM2.5 and their mass concentrations were both ~120 μg/m3 in the mature stage. In the early stage, the southerly sea breeze of ~4.5 m/s brought a large amount of moist air from the sea, which provided sufficient water vapor for the condensation and nucleation of pollutants, and thus accelerated the formation of haze. In the whole haze process, a weak updraft first appeared in the boundary layer, according to the vertical profile, contributing to the collision and growth of particulate matter. Vertical turbulence was barely observed in the mature stage, with the haze layer reaching 900 m in its peak, suggesting stable stratification conditions of the atmospheric boundary layer. The explosive growth of pollutant concentrations was about 10 h later than the formation of the stable stratification condition of the boundary layer. The potential source areas of air pollutants were identified by the WRF-FLEXPART model, which showed the significant contribution of local emissions and the transport effect of sea breeze. This study provides insights into the formation mechanism of haze pollution in this area, but the data observed in this campaign are also valuable for numerical modeling.
... Over the past 40 years, due to the large anthropogenic aerosol emission resulting from China's rapid urbanization and industrialization and complex secondary formation processes, the BTH region has become one of the most polluted regions throughout the country (Sun et al., 2018(Sun et al., , 2019(Sun et al., , 2021Zhai et al., 2019). ...
Aerosols could affect the thermal radiation process of planetary boundary layer (PBL), thus changing its thermodynamic and circulation structure and further triggering changes in aerosol concentrations. However, previous studies mostly focused on the direct health impacts of aerosols and meteorological conditions in the boundary layer and little is known about the health impacts associated with aerosol-PBL interactions. We quantitatively estimate the impacts of PM2.5 and air temperature on mortality caused by aerosol-PBL interactions. Utilizing the WRF-Chem model, we conducted a one-month numerical simulation during December 2015 in the Beijing-Tianjin-Hebei (BTH) region, one of the most polluted regions with the highest population density in China. We simulated PM2.5 concentrations and air temperature under two scenarios: with/without considering the aerosol-PBL interactions. Based on the simulation data, we adopted the distributed lag non-linear model (DLNM) to establish the exposure-response associations of PM2.5 and temperature with mortality and calculated the population-weighted exposure levels of PM2.5 and temperature. During December 2015 in the BTH region, the additional PM2.5-related and temperature-related deaths were 312 due to respiratory and circulatory disease. Without considering the aerosol-PBL interactions, the population-weighted average levels of both PM2.5 and temperature increased compared with the average levels. However, after considering the aerosol-PBL interactions, the population-weighted average temperature was decreased, while the population-weighted average PM2.5 concentration was increased. In the BTH region, more people lived in areas with high PM2.5 concentrations where have strong aerosol-PBL interactions. The aerosol-PBL interactions could intensify the adverse health impacts of temperature and PM2.5, which should not be neglected when to assess the health effects of aerosol and meteorological conditions.
... The NCP region is one of the economic zones in China, while most affected by air pollution Sun et al., 2021b). Carbonaceous aerosol is one of the main components of atmospheric particulate matter, which is of great significance due to its role in climate change, earth's radiation balance, visibility, and human health (Bikkina et al., 2017;Li et al., 2009;Wang et al., 2020b;Wang et al., 2020e). ...
Carbonaceous aerosol is one of the main components of atmospheric particulate matter, which is of great significance due to its role in climate change, earth's radiation balance, visibility, and human health. In this work, carbonaceous aerosols were measured in Shijiazhuang and Beijing using the OC/EC analyzer from December 1, 2019 to March 15, 2020, which covered the Coronavirus Disease 2019 (COVID-19) pandemic. The observed results show that the gas-phase pollutants, such as NO, NO2, and aerosol-phase pollutants (Primary Organic Compounds, POC) from anthropogenic emissions, were significantly reduced during the lockdown period due to limited human activities in North China Plain (NCP). However, the atmospheric oxidation capacity (Ox/CO) shows a significantly increase during the lockdown period. Meanwhile, additional sources of nighttime Secondary Organic Carbon (SOC), Secondary Organic Aerosol (SOA), and babs, BrC(370 nm) are observed and ascribed to the nocturnal chemistry related to NO3 radical. The Potential Source Contribution Function (PSCF) analysis indicates that the southeast areas of the NCP region contributed more to the SOC during the lockdown period than the normal period. Our results highlight the importance of regional nocturnal chemistry in SOA formation.
