Regional Air Quality Management Research Articles

Designating Airsheds in India for Urban and Regional Air Quality Management

Designating Airsheds in India for Urban and Regional Air Quality Management

Assessing the effectiveness of PM2.5 pollution control from the perspective of interprovincial transport and PM2.5 mitigation costs across China

Due to the transboundary nature of air pollutants, a province's efforts to improve air quality can reduce PM2.5 concentration in the surrounding area. The inter-provincial PM2.5 pollution transport could bring great challenges to related environmental management work, such as financial fund allocation and subsidy policy formulation. Herein, we examined the transport characteristics of PM2.5 pollution across provinces in 2013 and 2020 via chemical transport modeling and then monetized inter-provincial contributions of PM2.5 improvement based on pollutant emission control costs. We found that approximately 60% of the PM2.5 pollution was from local sources, while the remaining 40% originated from outside provinces. Furthermore, about 1011 billion RMB of provincial air pollutant abatement costs contributed to the PM2.5 concentration decline in other provinces during 2013–2020, accounting for 41.2% of the total abatement costs. Provinces with lower unit improvement costs for PM2.5, such as Jiangsu, Hebei, and Shandong, were major contributors, while Guangdong, Guangxi, and Fujian, bearing higher unit costs, were among the main beneficiaries. Our study identifies provinces that contribute to air quality improvement in other provinces, have high economic efficiency, and provide a quantitative framework for determining inter-provincial compensations. This study also reveals the uneven distribution of pollution abatement costs (PM2.5 improvement/abatement costs) due to transboundary PM2.5 transport, calling for adopting inter-provincial economic compensation policies. Such mechanisms ensure equitable cost-sharing and effective regional air quality management.

Spatiotemporal Distribution Characteristics and Inventory Analysis of Near-Road Traffic Pollution in Urban Areas

Vehicle emissions belong to the category of near-surface sources, occur close to human activity areas, and pose a greater threat to human health than other anthropogenic pollution sources. Furthermore, the study of the spatiotemporal characteristics of near-road traffic pollution is of great significance to urban and regional ambient air quality management, and is also an important basis for vehicle emission inventories, as well as the assessment of ambient air impact. Most previous studies have analyzed the spatiotemporal characteristics of hydrocarbons (HCs), carbon monoxide (CO), nitrogen oxides (NOx), and carbon dioxide (CO2) in urban vehicle emissions over a certain time, without considering the synergistic effect of mobile source particulate matter, NOx, and volatile organic compounds (VOCs). In this study, we analyze the composition of vehicles with different emission standards from road mobile sources in Anqing City, China. National category III and IV vehicles are the main contribution sources of various pollutants, accounting for more than 60% of emissions. Although national category I and II vehicles accounted for less than 1% of the total number of vehicles, their contribution to emissions cannot be ignored, especially for CO and HCs, the contribution of which from such vehicles can reach about 7%. This is mainly due to the low level of pollution control arising from the larger emission factor and greater age of these vehicles. Furthermore, eliminating old cars and increasing the proportion of national category VI vehicles can effectively reduce vehicle pollutant emissions. In terms of the spatiotemporal distribution characteristics, highways around urban areas are also the main sources of heavy vehicles, and the emission intensity of these pollutants is also higher on national roads and highways surrounding urban areas. In addition, the presence of m/p-xylene and toluene solvent-using species is detected, which indicates that petrol vehicle emissions, LPG and petrol volatilization, and solvent-using sources contribute significantly to ozone formation in the ozone pollution process. Comparing weekdays and non-weekdays, the PM2.5 peaks on non-weekdays are significantly higher than those on weekdays, and there is no “weekend effect”, which indicates that traffic emissions have little influence on PM2.5 emissions, and may be related to energy use and industrial pollution. Overall, this study strengthens the understanding of the relationship between emissions, traffic volumes, and vehicle types on spatial and temporal scales, and emphasizes the need for further investigation and comprehensive measures to mitigate pollution from these emissions.

Exposure to Ambient Particulate Matter during Pregnancy: Implications for Infant Telomere Length

Background: Growing evidence suggests that air pollution may influence fetal development, with potential consequences for later health. Alteration of telomere length (TL) is one possible mediating mechanism for the link between fetal exposure to air pollution and the development of disease. However, the few studies exploring associations between prenatal pollution and infant TL have assessed varied trimesters of pregnancy and shown mixed results. The aim of this study was to examine the differential relationships between prenatal exposure to air pollutant PM2.5 during the first, second, and third trimesters of pregnancy with infant TL at one month of age. Methods: Women (n = 74) were recruited in obstetric clinics during their third trimester. Data on PM2.5 exposure for each woman’s residential area during each trimester was acquired from the regional Air Quality Management District. At one month postnatal, a salivary sample was collected from the infant, which provided DNA for the telomere assay. Women completed questionnaires about stressors in their lives, perceived stress, depression, and sociodemographics for inclusion as covariates. Multiple linear regression was used to analyze the results. Results: PM2.5 exposure during the second (β = 0.31, p = 0.003) and third (β = 0.24, p = 0.02) trimesters was associated with longer infant TL. Exposure in the first trimester was not related to TL. Covariates of maternal depression and age and infant female sex were also associated with longer TL. Variables in the model contributed to 34% of the variance in TL (F = 10.58, p = 0.000). Discussion: Fetal programming of longer telomeres in response to pollution may have adaptive value in preparing the neonate for a postnatal environment that is less than optimal in terms of air quality. Alternatively, longer telomeres may forecast later health risks, considering established links between longer TL and diseases such as cancer. Future research needs to address how prenatal pollution interacts with TL to influence health over time.

Elucidating drivers of severe wintertime fine particulate matter pollution episodes in the Yangtze River Delta region of eastern China

Understanding the causes and sources responsible for severe fine particulate matter (PM2.5) pollution episodes that occur under conducive synoptic weather patterns (SWPs) is essential for regional air quality management. The Yangtze River Delta (YRD) region in eastern China has experienced recurrent severe PM2.5 episodes during the winters from 2013 to 2017. In this study, we employed an objective classification approach, the self-organizing map, to investigate the underlying impact of predominant SWPs on PM2.5 pollution in the YRD. We further conducted a series of source apportionment simulations using the Particulate Source Apportionment Technology (PSAT) tool integrated within the Comprehensive Air Quality Model with Extensions (CAMx) to quantify the source contributions to PM2.5 pollution under different SWPs. Here we identified six predominant SWPs over the YRD that are robustly connected to the evolution of the Siberian High. Considering the regional average PM2.5 anomalies, our results show that polluted SWPs favourable for the occurrence of regional PM2.5 pollution account for 61–78 %. The most conducive SWP, associated with the highest regional exceedance (46 %) of PM2.5 levels, is characterized by noticeable cyclonic anomalies at 850 hPa and stagnant surface weather conditions. Our source apportionment analysis emphasizes the pivotal role of local emissions and intra-regional transport within the YRD in shaping PM2.5 pollution in representative cities. Local emissions have the most significant impact on PM2.5 levels in Shanghai (32–48 %), while PM2.5 pollution in Nanjing, Hangzhou, and Hefei is more influenced by intra-regional transport (33–61 %). Industrial and residential emissions are the dominant sources, contributing 32–41 % and 24–38 % to PM2.5, respectively. Under specific SWPs associated with a stronger influence of inter-regional transport from northern China, there is a synchronously remarkable enhancement in the contribution of residential emissions. Our study pinpoints the opportunities for future air quality planning that would benefit from quantitative source attribution linked to prevailing SWPs.

Sources of air pollution-related health impacts and benefits of radially applied transportation policies in 14 US cities

As the world becomes increasingly urbanized, growing populations are exposed to poor ambient air quality and at risk of the associated health outcomes. Urban air quality is affected both by local sources of air pollution and sources outside city borders. Policy-makers who develop air quality policies need to know whether it is most effective to focus on local policies or to spend resources fostering larger regional air quality management cooperation. Identifying the fraction of air pollution exposure from emissions as a function of distance from the city is a critical element of air quality management design. We estimate the health burden associated with exposure to fine particulate matter (PM2.5), ozone (O3), and nitrogen dioxide (NO2) from county-level anthropogenic sources in and around 14 US cities; this analysis is a test-bed to conduct future global analyses. We use adjoint sensitivities calculated from the chemical transport model GEOS-Chem, high resolution satellite-derived surface concentrations of PM2.5 and NO2, and health impact assessment methods. For the 70.2 million people living in these cities, we estimate that 27,740 PM2.5- and O3-related premature deaths and 126,600 NO2-related new asthma cases were attributable to air pollution exposure in 2011. Development within the GEOS-Chem adjoint framework enables sectoral attribution and policy analysis in addition to the rote assessment of impact. We find that 70% of deaths and nearly 100% of these asthma cases were attributable to anthropogenic emissions. There is great variability in the sources of the anthropogenically-related health impacts; within-urban emissions make up 5% in Austin to 56% in Los Angeles and Phoenix (median: 31%) of urban premature deaths and 18% in Austin to 82% in Los Angeles (median: 59.5%) of new asthma cases, with the remaining portions attributable to emissions from outside the urban area. For each city, we estimate the air quality related health benefits associated with the adoption of a vehicle-miles-traveled fee in that city and in multiple spatial regions surrounding the city. The findings suggest that the proportion of urban air pollution that is regional is greater for premature deaths than new asthma cases and for the eastern US than the western US.

Surveillance of long-term environmental elements and PM2.5 health risk assessment in Yangtze River Delta, China, from 2016 to 2020.

PM2.5 metal pollution significantly harms human health. The air quality in Wuxi is poor, especially in winter, and long-term monitoring of PM2.5 elements comprising has not been performed previously. In the present study, 420 PM2.5 samples were collected from January 2016 to December 2020. Eleven elements, including Al, Mn, Ni, Cr, As, Cd, Sb, Hg, Pb, Se, and Tl, were analyzed by inductively coupled plasma mass spectrometry. The mean PM2.5 level was 56.1 ± 31.0μg/m3, with a tendency of yearly decreasing and a significant seasonal distribution variation. The concentration of 11 elements in the PM2.5 samples was 0.38 ± 0.33μg/m3. Al was the highest element with a range of 37.5-2148ng/m3. Meanwhile, the spatial distribution differences were compared by literatures review. Based on the Crystal Ball model, health risks were assessed dynamically using Monte Carlo uncertainty analysis. After 10,000 simulations, the mean value of the hazard index for nine elements was 0.743, and Mn contributed the most to the hazard index among elements, with a correlation of 0.3464. The average carcinogenic risk was 1.01 × 10-5, which indicated that the non-carcinogenic and carcinogenic risks were within the acceptable range. However, considerable attention should be paid to the potential health risks associated with long-term Al, Mn, and As exposure. This study provides detailed data on local atmospheric pollution characteristics, helps identify potential risk elements, and contributes to the development of effective regional air quality management.

Thermal Power Industry Emissions and Their Contribution to Air Quality on the Fen-Wei Plain

China has imposed an ultra-low emissions (ULE) standards policy regarding the emission concentrations of the thermal power industry, and the region of the Fen-Wei Plain (China), which is an important area regarding blue-sky protection, is in urgent need of accelerating the transformation of its thermal power units to conform with China’s ULE standards. Based on China’s high-resolution thermal power industry emission inventory, this study used the Comprehensive Air Quality Model with extensions (CAMX) to examine emissions from the thermal power industry and the associated impacts on the atmospheric environment of the Fen-Wei Plain. (1) Spatially, the highest emissions and the greatest air quality contributions were concentrated in the Shanxi and Henan areas, which are regions that have considerable power generation capacity. (2) In terms of species, NOX represented the largest air quality contribution and registered the smallest reduction. (3) Temporally, emission sources presented the greatest contributions in summer. (4) In terms of policy evaluation, implementation of ULE standards resulted in the reduction of the contribution of thermal power industry emissions to air quality by 56.82%, 27.44%, and 44.55% for SO2, NOX, and PM2.5, respectively. The results reflect the current levels of pollution on the Fen-Wei Plain attributable to the thermal power industry and provide insight in relation to decision-making processes regarding regional air quality management.

Design and implementation of a highly accurate spatiotemporal monitoring and early warning platform for air pollutants based on IPv6

The development of industry has brought about the pollution of the atmospheric environment. Pollution is harmful to people's health. Realizing the real-time monitoring of atmospheric environmental quality parameters can improve the above-mentioned effects. China's existing environmental monitoring systems focus on the accuracy of the system hardware itself for assessment, lack of data analysis and forecasting and early warning, and cannot provide managers and ordinary people with decision-making and activity guidance. This paper develops an IPV6-based high-spatial–temporal precision air pollutant monitoring and early warning platform. The feasibility of the system is verified through networking tests, operation tests, and early warning tests. Through actual data analysis and comparison, it is concluded that the monitoring system has field feasibility, and the atmospheric environment monitoring for the target observation area has achieved the desired observation function. This system integrates GIS technology and B/S architecture to analyze changes in the regional environment to provide support for regional environmental air quality management. The forecast and early warning module constructed by combining the weight method of the influence of different input factors on the environmental quality index and minute-level observations can provide technical support for the government to improve the level of supervision.

Air Quality Index Prediction Based on an Adaptive Dynamic Particle Swarm Optimized Bidirectional Gated Recurrent Neural Network–China Region

AbstractAccurate predictions of the air quality index (AQI) is critical for pollution control and air quality warning. However, this is challenging because of the nonlinearity of data and the uncertainty between data relationships. This paper proposes a combinatorial model based on an improved adaptive dynamic particle swarm optimization (ADPSO) algorithm to optimize a bidirectional gated recurrent unit (BiGRU) neural network to predict AQI time series and capture data dependence. The ADPSO method incorporates a dynamic spatial search strategy into the standard particle swarm optimization method, allowing the parameters to be dynamically adjusted to balance global and local search capabilities, thus improving the performance and effectiveness of this optimization process. Compared to the BiGRU model, the PSO‐BiGRU model, and the radial basis neural,the results of the improved algorithm reveal that the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of the ADPSO‐BiGRU predicted air pollution index are smaller than the errors of the other three models. The accuracy of the ADPSO‐BiGRU prediction model is higher than that of the other models, and it aids in the development of effective regional air quality management policies to reduce the negative impacts of pollution.

Heterogeneous spatiotemporal copula‐based kriging for air pollution prediction

AbstractAir pollutants have a significant negative impact on human health, especially cardiovascular and respiratory. An efficient and effective spatiotemporal model for air pollution prediction is urgently needed to help strengthen air pollution control and improve air quality. Empirical statistical models have been widely applied for spatiotemporal prediction. However, they are not capable of dealing well with space‐time heterogeneity and dependency, thereby achieving lower accuracy. Here, a hybrid frame named heterogeneous spatiotemporal copula‐based kriging is proposed for fine particulate matter (PM2.5) concentration prediction; it is a multi‐time, multi‐site kriging model based on spatiotemporal clustering. The proposed model is capable of addressing spatiotemporal heterogeneity effectively and accounting for the spatiotemporal dependence by copula‐based covariance functions. To evaluate the effectiveness of the proposed approach, an experiment on daily PM2.5 data in China in 2016 was carried out. The results (MAE = 6.48, RMSE = 10.68, = 0.919) show that the proposed model can achieve good performance in PM2.5 prediction, which is of great significance in regional air quality management.

Using SHERPA screening tool for design and assessment of local and regional air quality management

As atmospheric pollution continue to be a significant issue on global level, but with a particular importance at European Union level, the air pollution reduction policies will continue to be enforced in the next decades. At EU level, especially on most developed countries, NO2 and Particulate Matter (fine and coarse fractions) remain an acute problem. To help decision factors to have a better view on entire EU region the SHERPA (Screening for High Emission Reduction Potentials on Air quality) was developed, a tool that was proven helpful in addressing source allocation, governance and the assessment of scenario impacts.

Prediction of air particulate matter in Beijing, China, based on the improved particle swarm optimization algorithm and long short-term memory neural network

Intelligent algorithms using deep learning can help learn feature data with nonlinearity and uncertainty, such as time-series particle concentration data. This paper proposes an improved particle swarm optimization (IPSO) algorithm using nonlinear decreasing weights to optimize the hyperparameters, such as the number of hidden layer neurons, learning rate, and maximum number of iterations of the long short-term memory (LSTM) neural network, to predict the time series for air particulate concentration and capture its data dependence. The IPSO algorithm uses nonlinear decreasing weights to make the inertia weights nonlinearly decreasing during the iteration process to improve the convergence speed and capability of finding the global optimization of the PSO. This study addresses the limitations of the traditional method and exhibits accurate predictions. The results of the improved algorithm reveal that the root means square, mean absolute percentage error, and mean absolute error of the IPSO-LSTM model predicted changes in six particle concentrations, which decreased by 1.59% to 5.35%, 0.25% to 3.82%, 7.82% to 13.65%, 0.7% to 3.62%, 0.01% to 3.55%, and 1.06% to 17.21%, respectively, compared with the LSTM and PSO-LSTM models. The IPSO-LSTM prediction model has higher accuracy than the other models, and its accurate prediction model is suitable for regional air quality management and effective control of the adverse effects of air pollution.

Formation mechanism of a severe air pollution event: A case study in the Sichuan Basin, Southwest China

The formation mechanism of air pollution events in the Sichuan Basin (SB), which is the fourth most heavily polluted area in China, has not been fully revealed. This study investigated the formation mechanism of a severe air pollution event over the SB using synoptic approaches and model simulations. The results can be summarized as follows: (1) Heavy air pollution in the SB was characterized by low visibility, low atmospheric boundary layer (ABL) height, high temperature, high relative humidity, strong temperature inversion layer, subsidence in the troposphere, high water vapor content between 500 and 900 hPa, southerly winds in the low troposphere, and surface winds with low speed and irregular direction. (2) Air quality in the SB was closely related to the weather system at 700 hPa over the basin. When the 700 hPa weather system affecting the SB was a high-pressure system, the subsidence and stable atmospheric stratification increased the air pollutant concentrations near the ground. When the 700 hPa weather system affecting the SB was a low-pressure system and the basin was in front of this low-pressure system, southwesterly warm and moist airflow and adiabatic subsidence warming formed the thick temperature inversion layer over the basin. As a result, the temperature inversion layer trapped air pollutants in the basin and induced the heavy air pollution event. When the 700 hPa weather system over the SB was a low-pressure system and the basin was behind the low-pressure system, the dry and cold airflow from the north invaded southward to the basin and broke the temperature inversion layer. Consequently, air pollutants dispersed vertically, resulting in decreased concentrations near the ground. (3) Air pollutants from December 17, 2017 to January 4, 2018 were mainly from local emissions. (4) The WRF-Chem model not only reproduced the variations in PM2.5 concentrations, the ABL height, and the height-time cross-sections of temperature, water vapor content, and wind over Chengdu during the air pollution event, but also revealed the formation mechanism of this heavy air pollution event. The results of this study reveal the formation mechanism of winter heavy air pollution events over the SB and help develop effective regional air quality management strategies to reduce the likelihood of local air pollution events and minimize the adverse impacts of air pollution.

National Clean Air Programme (NCAP) for Indian cities: Review and outlook of clean air action plans

National Clean Air Programme (NCAP), launched in 2019, is India's flagship program for better air quality in 122 cities. This review evaluates the scientific, legislative, financial, and institutional framework of the 102 publicly available clean air action plans submitted under NCAP. We assessed the robustness of the plans using the background information on pollution sources and their contribution; legal backing for the clean air action plan; cost of measures; and existing institutional accountability regime. We used the tally chart method for estimating city-specific and sector-specific mitigation measures, the number of actions under the purview of various implementation agencies, and the number of institutional, physical, and promotional interventions in the plans. Transport and road dust together cover 50% of action points, followed by interventions for the industries. Domestic cooking and heating is mentioned as a source only in 42 plans for a total of 2% of the action points. Institutional nature of the interventions was observed in 74% of the action points, using the language “overseeing, planning, proposing, preparing, investigating, identifying, ensuring, strengthening, training, studying, and engaging”. We also identified the plans that contained information on source contributions, an outline of financial requirements for executing the plans and measures for mitigating pollution from regional sources. Only 25% of the plans integrated information on the relative source contributions to formulate control strategies. Even fewer plans outlined the financial requirements for executing the plans. The institutional and administrative arrangements for ensuring inter-departmental and regional alignment in air pollution mitigation strategies are absent. We close the review with recommendations which include mandating regular updates for emission and pollution loads, granting greater fiscal autonomy for Urban Local Bodies to maintain the infrastructure necessary for sustaining air quality benefits, and moving from city-centric to airshed-centric air quality management. • Review of India's 102 publicly available city clean air action plans. • Review of the institutional, legal, and administrative arrangements for regional air quality management. • Review of availability of city-specific information on source contributions and financial commitments. • NCAP requires a central executor with legal mandate to prioritize actions and an institutional mechanism to track progress.

Spatiotemporal trends of PM2.5 concentrations and typical regional pollutant transport during 2015–2018 in China

China has suffered extremely serious and sustained haze in recent years, and this haze has led to growing public concern about air quality. Based on the data from 1497 national environmental monitoring stations, the kriging interpolation method and HYSPLIT model was used to analyze spatiotemporal trends and typical regional pollutant transport in China from 2015 to 2018. The results indicated that the average PM2.5 concentration in China reached 44.43 μg·m−3, and the proportion of areas with levels below Chinese Ambient Air Quality Standards (CAAQS) Grade II (35 μg·m−3) increased in the past 4 years from 31.70% to 53.70%, particularly in the North China Plain (NCP). The PM2.5 concentrations in country and divisions of NCP, Junggar Basin (JB), other regions of China (ORC) showed a U-shaped seasonal and monthly trend of high values in winter and low values in summer and a yearly trend of continuous decline. Tarim Basin (TB) was affected by increasing dust fall with the highest values in spring, and the Fenwei Plain (FWP) was affected by combustion and topography with increases in spring and winter during study period. Furthermore, SJZ (Shijiazhuang) and XA (Xi'an) were selected as representative cities in the NCP and FWP to study regional pollutant transport. And the pollution in the XA mainly comes from local sources in all seasons, while the pollution in the SJZ is mainly from Shanxi Province in winter and from local sources in summer. The findings provide a useful reference for improving regional air quality management.

Refined assessment of size-fractioned particulate matter (PM2.5/PM10/PMtotal) emissions from coal-fired power plants in China

Chinese coal-fired power plants (CFPPs) are experiencing large-scale and rapid retrofitting of ultralow emission (ULE), causing significant changes in emission level of particulate matter (PM) from CFPPs. In this study, based on coal ash mass balance over the whole process, an integrated emission factors (EFs) database of three size-fractioned particulate matters (PM2.5, PM10, and PMtotal) for CFPPs is constructed, which covers almost all typical ULE technical routes installed in CFPPs. To verify the reliability of PM EFs established in this study, we compare those with related results based on field tests. Overall, the gaps in the EFs of PM2.5, PM10, and PMtotal obtained by the two methods are not outrageous within a reasonable range. By combined with the refined size-fractioned PM EFs and unit-based activity level database, a detailed high-resolution emission inventory of PM2.5, PM10, and PMtotal from Chinese CFPPs in 2017 is established, with the corresponding total emissions of 143, 207, and 267 kt, respectively. Our estimation of PMtotal emission is comparable to the official statistics announced by China Electricity Council (CEC), which further demonstrates the reliability of PM EFs constructed in this study. Moreover, potential reductions of PM from CFPPs at two stages before and after 2017 are assessed under three application scenarios of major ULE technical routes. We forecast the final annual emissions of PM2.5, PM10, and PMtotal until 2020 will be reduced further, which fall within the range of 86–111 kt, 120–157 kt, and 142–184 kt, respectively, if all CFPPs achieve ULE requirements under the three scenarios. We believe our integrated database of PM EFs of CFPPs has good universality, and the forecast results will be helpful for policy guidance of ULE technologies, emissions inventory compilation, and regional air quality simulation and management.

Spatial determination of traffic CO emissions within street canyons using inverse modelling

Local and regional air quality management plans in Europe are used to stablish legal mechanisms for emission standards. We present a robust method combining spatial analysis with a dispersion model - Operational Street Pollution Model - (OSPM) for the estimation of direct contaminants (CO) emitted by vehicles in streets canyons (SC) in the city of Valencia. This method includes the use of an inverse model to obtain emission factor (EF) base on experimental data. The results show the strong influence of the traffic intensity data (TID) and wind velocity (WV) with traffic CO emissions. This method is used to analyze the influence of applying corrective actions in the city of Valencia to reduce pollution levels, such as a reduction of traffic intensity or a variation of the emission factor. This procedure can be used in other geographical zones adapting the geographical information and the emission factor.

Spatio-temporal evolution and the influencing factors of PM2.5 in China between 2000 and 2015

High concentrations of PM2.5 are universally considered as a main cause for haze formation. Therefore, it is important to identify the spatial heterogeneity and influencing factors of PM2.5 concentrations for regional air quality control and management. In this study, PM2.5 data from 2000 to 2015 was determined from an inversion of NASA atmospheric remote sensing images. Using geo-statistics, geographic detectors, and geo-spatial analysis methods, the spatio-temporal evolution patterns and driving factors of PM2.5 concentration in China were evaluated. The main results are as follows. (1) In general, the average concentration of PM2.5 in China increased quickly and reached its peak value in 2006; subsequently, concentrations remained between 21.84 and 35.08 μg/m3. (2) PM2.5 is strikingly heterogeneous in China, with higher concentrations in the north and east than in the south and west. In particular, areas with relatively high PM2.5 concentrations are primarily in four regions, the Huang-Huai-Hai Plain, Lower Yangtze River Delta Plain, Sichuan Basin, and Taklimakan Desert. Among them, Beijing-Tianjin-Hebei Region has the highest concentration of PM2.5. (3) The center of gravity of PM2.5 has generally moved northeastward, which indicates an increasingly serious haze in eastern China. High-value PM2.5 concentrations have moved eastward, while low-value PM2.5 has moved westward. (4) Spatial autocorrelation analysis indicates a significantly positive spatial correlation. The “High-High” PM2.5 agglomeration areas are distributed in the Huang-Huai-Hai Plain, Fenhe-Weihe River Basin, Sichuan Basin, and Jianghan Plain regions. The “Low-Low” PM2.5 agglomeration areas include Inner Mongolia and Heilongjiang, north of the Great Wall, Qinghai-Tibet Plateau, and Taiwan, Hainan, and Fujian and other southeast coastal cities and islands. (5) Geographic detection analysis indicates that both natural and anthropogenic factors account for spatial variations in PM2.5 concentration. Geographical location, population density, automobile quantity, industrial discharge, and straw burning are the main driving forces of PM2.5 concentration in China.

Spatial self-aggregation effects and national division of city-level PM2.5 concentrations in China based on spatio-temporal clustering

With growing haze episodes in China, comprehensive air quality management has been frequently proposed and implemented during major events or heavy pollution episodes. However, except for such heavily polluted regions as the Beijing-Tianjin-Hebei region, regional integration of air quality management in other parts of China has rarely been discussed, due to limited research on the spatio-temporal aggregation of PM2.5 concentrations. To fill this gap, we employed a repeated-bisection method, which supports high dimensional datasets and bootstrap clustering, for spatio-temporal clustering of city-level PM2.5 concentrations in China using time-series PM2.5 data and the test of geographical detector proved the reliability of the clustering. Since no weighted geographical information was employed during the clustering process, this research suggested that PM2.5 concentrations in China were of strong spatial self-aggregation effects, which proved the necessity for regional integration of air quality management. Based on the spatio-temporal clustering of PM2.5 concentrations, we further proposed six divisions of PM2.5 concentrations across China, within which PM2.5 concentrations display similar variation patterns and specific emission-reduction measures can be implemented accordingly. The division output of PM2.5 concentrations was highly consistent with the recent “2017 air pollution prevention and management plan for the Beijing-Tianjin-Hebei region and its surrounding areas” plan, indicating the reliability and practical significance of the national division of PM2.5 concentrations based on spatio-temporal clustering. The findings and methodology from this research provide useful reference for improving regional air quality management by better understanding spatio-temporal aggregation of PM2.5 concentrations.