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The location of Anhui Province (up-left), Hefei (down-left), and 10 air quality stations of Hefei (right).
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Spatiotemporal behaviors of particulate matter (PM2.5 and PM10) and trace gases (SO2, NO2, CO, and O3) in Hefei during the period from December 2013 to November 2015 are investigated. The mean annual PM2.5 (PM10) concentrations are 89.1 ± 59.4 µg/m3 (118.9 ± 66.8 µg/m3) and 61.6 ± 32.2 µg/m3 (91.3 ± 40.9 µg/m3) during 2014 and 2015, respectively, r...
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The National Capital Region of Delhi in India is one of the most polluted urban areas in the world and its intense pollution episodes are attributed to crop residue burning (CRB). However, existing studies are limited in time and pollutant type and do not often control for non-biophysical factors. We used ground-measured pollutant levels and Aeroso...
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... The nanoparticles have the potential to penetrate through the skin as well as the blood vessels due to their smaller size (Møller et al. 2020). The major health impacts of exposure to these smaller-size nanoparticles are stroke, hypertension, and myocardial infarction Karl et al. 2020;Mao et al. 2020;Schraufnagel 2020 ...
Diwali, the fireworks festival of India, adds more atmospheric particles within a short period of time and deteriorates the air quality. Short-term policies like banning crackers during fireworks festivals can help improve urban air quality. The present study analyzed particle number concentration, ranging from 10 to 1000 nm, in 2021 and 2022. A reduction in the concentration of particle number concentration (from 3.8 × 10⁴ cm⁻³ to 3.1 × 10⁴ cm⁻³) was observed due to the ban on crackers in the urban city of Delhi. The concentration range changes from 10⁵ cm⁻³ to 10⁴ cm⁻³. The contribution of different size ranges, Nucleation (10 to 30 nm), Aitken (30 to 100 nm), and Accumulation (100 to 1000 nm) are analyzed. During Diwali day, the Accumulation mode particles contribute to around 60% to 83% to the total particle number concentration. The exposure to total inhalable particle concentration on Diwali (During ban on firecrackers) was reduced by about 18%, i.e., 1.6 million particles per day. The study results show that emissions in urban regions can be reduced significantly by proper implementation of policy and participation from citizens. Reducing particle emissions paves the way for air quality improvement, health impact mitigation, and sustainability. Sustainability goals focus on clean air for all, and health improvement in polluted regions as interim goals, that can be achieved by implementing proper mitigation measures, which consequently help fight climate change.
... Both PSCF and CWT analysis is run by the MeteoInfo soltware-TrajStat Plugin. More information about PSCF and CWT can be found elsewhere [40][41][42][43][44][45][46][47]. (Table S1), the proximate grid was picked out from MERRA-2, and the corresponding reanalysis values were adopted for validation. ...
Black carbon (BC), an important component of atmospheric aerosol, plays a significant role in regional climate, hydrological cycle, variation of monsoon rainfall, and human health. The 40-year detailed atmospheric BC over China from 1981 to 2020 is systematically investigated through the MERRA-2 reanalysis dataset. MERRA-2 BC generally showed a good correlation (R = 0.68) compared with 673 monthly samples from ground-based observation at 35 stations around China. The overall annual average of MERRA-2 BC concentration over China is 1.15 μg/m3, with a fast growth rate during 1981–2007 and a relatively slow decrease after that. The winter season has the highest mean concentration of BC, followed by autumn and spring, whereas summer shows relatively weaker values. The order of annual average BC concentrations during 1981–2020 is Beijing-Tianjin-Hebei region (BTH, 4.02 μg/m3) > Sichuan Basin (SB, 3.94 μg/m3) > Yangtze River Delta (YRD, 2.68 μg/m3) > Pearl River Delta (PRD, 1.47 μg/m3). The monthly mean BC concentrations over the BTH, YRD, PRD and SB are estimated to be smallest 3.18 μg/m3 in May, 1.94 μg/m3 in August, 0.82 μg/m3 in July, 3.04 μg/m3 in June, respectively, whilst largest consistently in December with 5.09 μg/m3, 3.83 μg/m3, 2.12 μg/m3, and 5.41 μg/m3, respectively. Our study indicates the primary potential source areas for BC are concentrated in the research city and its surroundings. Beijing and Chengdu are more BC-polluted areas than Shanghai and Guangzhou. Long-distance, regional transfer from south BTH contributes importantly to BC pollution in Beijing under the influence of prevailing southerly winds. The geographical location of Chengdu causes the transport and accumulation of BC inside the SB.
... In addition, it also has direct and indirect impacts on weather and climate, and atmospheric visibility [1]. A prolonged existence of haze days (few days to weeks) in an urban environment (which is referred to as a haze episode) can be induced by the synergistic effect of multiple interrelated factors, which include anthropogenic emission, secondary aerosols [5,6], longrange transport [7][8][9], unfavorable local and synoptic meteorological conditions [10][11][12]. The effects of different meteorological processes on haze pollution are inconsistent and can have contrasting effects [11]. ...
Citation: Aman, N.; Manomaiphiboon, K.; Pala-En, N.; Devkota, B.; Inerb, M.; Kokkaew, E. A Study of Urban Haze and Its Association with Cold Surge and Sea Breeze for Greater Bangkok. Int. J. Environ. Res. Public Health 2023, 20, 3482. https://doi.org/10.3390/ ijerph20043482 Academic Editors: Isidro A. Pérez and M. Ángeles García Abstract: This study deals with haze characteristics under the influence of the cold surge and sea breeze for Greater Bangkok (GBK) in 2017-2022, including haze intensity and duration, meteorological classification for haze, and the potential effects of secondary aerosols and biomass burning. A total of 38 haze episodes and 159 haze days were identified. The episode duration varies from a single day to up to 14 days, suggesting different pathways of its formation and evolution. Short-duration episodes of 1-2 days are the most frequent with 18 episodes, and the frequency of haze episodes decreases as the haze duration increases. The increase in complexity in the formation of relatively longer episodes is suggested by a relatively higher coefficient of variation for PM 2.5. Four meteorology-based types of haze episodes were classified. Type I is caused by the arrival of the cold surge in GBK, which leads to the development of stagnant conditions favorable for haze formation. Type II is induced by sea breeze, which leads to the accumulation of air pollutants due to its local recirculation and development of the thermal internal boundary layer. Type III consists of the haze episodes caused by the synergetic effect of the cold surge and sea breeze while Type IV consists of short haze episodes that are not affected by either the cold surge or sea breeze. Type II is the most frequent (15 episodes), while Type III is the most persistent and most polluted haze type. The spread of haze or region of relatively higher aerosol optical depth outside GBK in Type III is potentially due to advection and dispersion, while that in Type IV is due to short 1-day episodes potentially affected by biomass burning. Due to cold surge, the coolest and driest weather condition is found under Type I, while Type II has the most humid condition and highest recirculation factor due to the highest average sea breeze duration and penetration. The precursor ratio method suggests the potential effect of secondary aerosols on 34% of the total haze episodes. Additionally, biomass burning is found to potentially affect half of the total episodes as suggested by the examination of back trajectories and fire hotspots. Based on these results, some policy implications and future work are also suggested.
... The values of daily mean PM2.5 concentrations for three cities in 2018 are adopted in the CWT maps. The CWT model is applied to weight-back trajectories (four times per day, at 0:00, 6:00, 12:00, and 18:00 UTC) with related PM2.5 concentrations for unraveling the relative contribution of different sources-areas [30][31][32][33]. Areas with high CWT values are considered as potential geographic origins of PM2.5. ...
... The seasonal PM patterns in Qingdao and Hangzhou are similar, which are slightly different from that in Xiamen. The increased energy consumption associated with fossil fuels and biomass combustion could lead to a high level of PM mass concentrations in the cold season [32]. Severe PM loadings are seen in winter over Qingdao (53.7 μg/m 3 for PM2.5 and 100.7 μg/m 3 for PM10) and Hangzhou (57.1 μg/m 3 for PM2.5 and 86.4 μg/m 3 for PM10), in association with combined effects of strong particle emissions and stable weather conditions in winter [41,42], whereas Xiamen has a maritime climate with mild winter temperatures, showing high PM levels (26. ...
Recently released hourly particular matter (PM:PM2.5 and PM10) and gaseous pollutants (SO2, NO2, CO, and O3) data observed in Qingdao, Hangzhou, and Xiamen from 2015 to 2019 were utilized to reveal the current situation of air pollution over eastern coastal China. The PM pollution situation over the three metropolises ameliorated during studied period with the concentrations decreasing about 20–30%. Gas pollutants, excepting SO2, generally exhibit no evident reduction tendencies, and a more rigorous control standard on gaseous pollutants is neededEven for the year 2018 with low pollution levels among the study period, these levels (
... In terms of multiple indicators, there are few researchers and different perspectives. Some scholars (Xie et al., 2015;Mao et al., 2020) selected PM2.5, PM10, SO2, NO2, CO and O3 for research. Shi et al. (2020) selected PM2 5 and meteorological parameters. ...
... In terms of the selection of research objects, there are more achievements in single cities and multiple cities, but less in urban agglomerations. Some scholars (Huang et al., 2015;Mao et al., 2020;Zhang et al., 2020;Yu et al., 2021) have studied single cities or provinces in China. The research of most scholars (Xie et al., 2015;Jassim and Coskuner, 2017;Fan et al., 2019;Chen X. et al., 2020;Dai et al., 2020;Wang et al., 2020;Zhao et al., 2020) is based on the overall region. ...
With rapid population growth and productivity development, the contradiction between economic and social development and resource and environmental protection is becoming increasingly prominent, so it is important to study the regional environmental carrying capacity to protect the environment and promote high-quality economic development. This study takes the three major urban agglomerations of Beijing-Tianjin-Hebei, Yangtze River Delta and Pearl River Delta as the research objects, and uses the entropy-weighted topsis model and the obstacle diagnosis model to evaluate the comprehensive environmental carrying capacity levels of the three major urban agglomerations and analyse their main obstacle factors. The results show that: 1) the average environmental carrying capacity level scores of the three major urban agglomerations during the study period were Beijing-Tianjin-Hebei (0.23103) > Yangtze River Delta (0.17687) > Pearl River Delta (0.16); 2) the degree of synergy between subsystems affects the environmental carrying capacity level; 3) China has still not achieved harmony between the environment and economic growth, and economic development is the main influencing factor for the level of environmental carrying capacity. In the future, it is recommended that each city cluster adhere to the construction of ecological civilization and vigorously develop high-tech and green industries; at the same time, give full play to the radiation-driven role of the regional core cities, make use of the synergistic effect of resource agglomeration and maximize the efficiency of resource utilization, so as to ultimately achieve the coordinated development of economic society and regional resources and environment.
... In terms of multiple indicators, there are few researchers and different perspectives. Some scholars (Xie et al., 2015;Mao et al., 2020) selected PM2.5, PM10, SO2, NO2, CO and O3 for research. Shi et al. (2020) selected PM2 5 and meteorological parameters. ...
... In terms of the selection of research objects, there are more achievements in single cities and multiple cities, but less in urban agglomerations. Some scholars (Huang et al., 2015;Mao et al., 2020;Zhang et al., 2020;Yu et al., 2021) have studied single cities or provinces in China. The research of most scholars (Xie et al., 2015;Jassim and Coskuner, 2017;Fan et al., 2019;Chen X. et al., 2020;Dai et al., 2020;Wang et al., 2020;Zhao et al., 2020) is based on the overall region. ...
Beijing-Tianjin-Hebei urban agglomeration (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD) are the most important economic hinterlands in China, offering high levels of economic development. In 2020, their proportion of China’s total GDP reached 39.28%. Over the 5 years of 2014–2018, the annual maximum air quality index (AQI) of the three major urban agglomerations was greater than 100, thus maintaining a grade III light pollution (100 < AQI < 200) in Chinese air standards. This research thus uses a two-stage empirical analysis method to explore the spatial-temporal dispersal physiognomies and spillover effects of air quality in these three major urban agglomerations. In the first stage, the Kriging interpolation method regionally estimates and displays the air quality monitoring sampling data. The results show that the air quality of these three major urban agglomerations is generally good from 2014 to 2018, the area of good air is gradually expanding, the AQI value is constantly decreasing, the air pollution of YRD is shifting from southeast to northwest, and the air pollution of PRD is increasing. The dyeing industry shows a trend of concentration from northwest to south-central. In the second stage, Moran’s I and Spatial Durbin Model (SDM) explore the spatial autocorrelation and spillover effects of air quality related variables. The results show that Moran’s I values in the spatial autocorrelation analysis all pass the significance test. Moreover, public transport, per capita GDP, science and technology expenditure, and the vegetation index all have a significant influence on the spatial dispersal of air quality in the three urban agglomerations, among which the direct effect of public transport and the indirect effect and total effect of the vegetation index are the most significant. Therefore, the China’s three major urban agglomerations (TMUA) ought to adjust the industrial structure, regional coordinated development, and clean technology innovation.
... As shown in Fig. 6b and Fig. 6d, the model presents a pattern of high PM 2.5 in the Yangtze River Delta, specifically in Anhui, Jiangsu, Zhejiang and northern Jiangxi, southwestern Shandong, eastern Henan and southeastern Shanxi, which are identified as common high potential source areas for PM 2.5 . This pattern was in a good agreement with a previous study, which also showed that air pollution in Hefei city during 2013-2015 was often influenced by local primary emissions, secondary formation, and regional transport from neighboring cities and remote areas (Mao et al., 2020). ...
Atmospheric particulate matter, especially in urban and industrial environments, can act as a source of different organic pollutants that can pose significant health impacts to residents. However, the pollution status and transport mechanisms of fine particle-bound polycyclic aromatic hydrocarbons (PAHs) in indoor and outdoor environments are uncertain. This study aimed to determine the spatial distribution and morphological characteristics of fine particle-bound PAHs and analyze the factors (source contributions and backward trajectories) that influence their concentrations. The results showed that mean concentrations of 16 PAHs were higher in indoor dust as compared to outdoor dust. In addition, the lowest concentrations of the 16 PAHs were found on the 11-20th floor, with smoking households > nonsmoking households (except Nap, Acy, and Ace). The 2–3 ring PAHs were more prominent in households with cooking activities. The particle size distribution showed that most of the particles were <62 μm in diameter, indicating that the indoor particles were smaller in size. Furthermore, the range of δ13C values in the outdoor dust (−30.17 ~ −28.63 ‰) samples was significantly lower than in indoor dust (−28.29 ~ −22.53 ‰). The results based on diagnostic ratios, positive matrix factorization (PMF) analysis and backward trajectory model analysis suggested that the sources of PAHs in indoor and outdoor dust were mixed, originated both locally and from neighboring provinces transported over long distances, especially concentrated in the Yangtze River Delta area. Finally, carcinogenic risk values for indoor dust were greater than those for outdoor dust. Therefore, it is recommended that local governments and industries with high PAH emissions should implement proper protocols to monitor and minimize the pollution levels of PAHs in the urban industrial environment in order to mitigate their health risks.
... At the same time, the potential source area of PM 2.5 was verified again using concentrationweighted trajectory (CWT) [24]. The difference between CWT and PSCF is that PSCF uses a concentration threshold to evaluate the potential source of PM 2.5 . ...
Using the hourly monitoring data of pollutants from 16 automatic atmospheric monitoring stations in eastern Jilin Province from 2015 to 2020, this paper analyzed the temporal and spatial distribution laws of CO, SO2, NO2, PM10, PM2.5, and O3 in eastern Jilin Province. At the same time, the regional transport pathways of pollutants were analyzed using the hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model; the potential source contribution function (PSCF) analyzed the potential source area of PM2.5. Finally, the “weekend effect” of CO, NO2, PM2.5, and O3 was analyzed. The results showed that the six pollutants showed a downward trend year by year. The concentrations of O3, PM10, and PM2.5 were higher in northwest Jilin, and the concentrations of SO2 and CO were higher in southwest Jilin. Except for CO, the seasonal variation of pollutants was pronounced. Except for O3, most pollutants had the highest concentration in winter. Hourly variation analysis described that SO2 and O3 had only one peak in a day, and the other four pollutants showed “double peak” hourly variation characteristics. The study area was mainly affected by the airflow pathway from northwest and southwest. The weight potential source contribution function (WPSCF) high-value area of PM2.5 was northwest and southwest. O3 showed a “negative weekend effect”, and NO2 and CO showed a “positive weekend effect”.
... In fact, the parallel trend test was just a necessary precondition for the application of DID. The conformance of the study object to the postulated condition did not mean that the results of the coefficient of the interaction variable reflected the policy effect more than other effects (Guo et al., 2020;Liu et al., 2020;Mao et al., 2020). One important consideration regards the influence of uncontrolled regional time-varying characteristics and undetected factors on estimation results. ...
Evaluating the price policy of raw milk is of great significance to the sustainable development of an industry supply chain. In this context, our study used the multi-period difference-in-difference method to systematically examine the impact of the policy implementation on product price and profit distribution in the supply chain. The results showed the following: (1) the price of raw milk in the implementation area of the price support policy is 13.54% higher than that of the unimplemented area; (2) the effect of price increase in the western region (15.5%) is higher than that in the eastern region (13%), and the central region (10.73%); (3) furthermore, the purchase price guidance policy of raw milk drives price increase or price suppression in the links of the supply chain to promote a balanced distribution of profits among the participants in the chain. These conclusions all have good stability and have reference significance for further improving and adjusting the price support policy of raw milk to realize the sustainable development of the Chinese dairy industry. This will enhance the production confidence of Chinese raw milk producers and improve Chinese consumers’ expectations and consumer psychology regarding domestic dairy products.
... Current studies mainly discuss dairy trade based on a single country (Kurata and Ohe, 2020;Liu et al., 2020) or on two countries (Guo et al., 2020;Mao et al., 2020). In terms of content, literature has mainly focused on the following areas: the competitiveness of dairy trade (Khan et al., 2020), trade potential analysis (Sánchez-López et al., 2020), influencing factors (Bogadóttir, 2020), countermeasures (Zhao et al., 2020), and the impact on industry and market development (Peng and Cox, 2006;Zhang et al., 2020). ...
This study conducted a social network analysis of the evolutionary characteristics of the world dairy trade network based on the overall trade pattern. In addition, the evolution of trade blocs and the co-opetition relationships involving dairy products in major countries were analyzed in terms of supply and demand. The results show that continuous and complex changes have taken place in the world’s dairy trade network since 2001. The number of trade entities in dairy products has stabilized since 2012. At present, approximately 94% of countries (regions) are involved in dairy product trade, such that the world dairy trade network exhibits the small-world effect and scale-free property. The world import pattern for dairy products has changed. While export centers have not changed, import centers have shifted from Europe, America, and East Asia to North America, East Asia, and the Middle East. The world dairy trade network consists of the EU trade bloc headed by Germany, the former Soviet Union–Brazil trade bloc, and the Asia–Australia–America trade bloc. The trade blocs have evolved due to geographical positions, historical cultures, and political relations. In a trade bloc, the diversification of import sources is more prominent in demand countries. European and Asian markets have become the main markets of the major exporters. In this study, the evolutionary characteristics of the world dairy trade network and the co-opetition relationships were analyzed to provide scientific support to inform the development of dairy trade policies. The results can provide technical and psychological support to policy-makers in various countries in their dairy trade decision-making.
