Machine learning insights into PM2.5 changes during COVID-19 lockdown: LSTM and RF analysis in Mashhad.

This study seeks to investigate the impact of COVID-19 lockdown measures on air quality in the city of Mashhad employing two strategies. We initiated our research using basic statistical methods such as paired sample t-tests to compare hourly PM2.5 data in two scenarios: before and during quarantine, and pre- and post-lockdown. This initial analysis provided a broad understanding of potential changes in air quality. Notably, a low reduction of 2.40% in PM2.5 was recorded when compared to air quality prior to the lockdown period. This finding highlights the wide range of factors that impact the levels of particulate matter in urban settings, with the transportation sector often being widely recognized as one of the principal causes of this issue. Nevertheless, throughout the period after the quarantine, a remarkable decrease in air quality was observed characterized by distinct seasonal patterns, in contrast to previous years. This finding demonstrates a significant correlation between changes in human mobility patterns and their influence on the air quality of urban areas. It also emphasizes the need to use air pollution modeling as a fundamental tool to evaluate and understand these linkages to support long-term plans for reducing air pollution. To obtain a more quantitative understanding, we then employed cutting-edge machine learning methods, such as random forest and long short-term memory algorithms, to accurately determine the effect of the lockdown on PM2.5 levels. Our models' results demonstrated remarkable efficacy in assessing the pollutant concentration in Mashhad during lockdown measures. The test set yielded an R-squared value of 0.82 for the long short-term memory network model, whereas the random forest model showed a calculated cross-validation R-squared of 0.78. The required computational cost for training the LSTM and the RF models across all data was 25 min and 3 s, respectively. In summary, through the integration of statistical methods and machine learning, this research attempts to provide a comprehensive understanding of the impact of human interventions on air quality dynamics.

Spatio-temporal fusion of meteorological factors for multi-site PM2.5 prediction: A deep learning and time-variant graph approach.

In the field of environmental science, traditional methods for predicting PM2.5 concentrations primarily focus on singular temporal or spatial dimensions. This approach presents certain limitations when it comes to deeply mining the joint influence of multiple monitoring sites and their inherent connections with meteorological factors. To address this issue, we introduce an innovative deep-learning-based multi-graph model using Beijing as the study case. This model consists of two key modules: firstly, the 'Meteorological Factor Spatio-Temporal Feature Extraction Module'. This module deeply integrates spatio-temporal features of hourly meteorological data by employing Graph Convolutional Networks (GCN) and Long Short-Term Memory (LSTM) for spatial and temporal encoding respectively. Subsequently, through an attention mechanism, it retrieves a feature tensor associated with air pollutants. Secondly, these features are amalgamated with PM2.5 concentration values, allowing the 'PM2.5 Concentration Prediction Module' to predict with enhanced accuracy the joint influence across multiple monitoring sites. Our model exhibits significant advantages over traditional methods in processing the joint impact of multiple sites and their associated meteorological factors. By providing new perspectives and tools for the in-depth understanding of urban air pollutant distribution and optimization of air quality management, this model propels us towards a more comprehensive approach in tackling air pollution issues.

Spatio-temporal evolution and influencing factors of synergizing the reduction of pollution and carbon emissions - Utilizing multi-source remote sensing data and GTWR model.

Grasping current circumstances and influencing components of the synergistic degree regarding reducing pollution and carbon has been recognized as a crucial part of China in response to the protection of the environment and climate mitigation. With the introduction of remote sensing night-time light, CO2 emissions at multi-scale have been estimated in this study. Accordingly, an upward trend of "CO2-PM2.5" synergistic reduction was discovered, which was indicated by an increase of 78.18% regarding the index constructed of 358 cities in China from 2014 to 2020. Additionally, it has been confirmed that the reduction in pollution and carbon emissions could coordinate with economic growth indirectly. Lastly, it has identified the spatial discrepancy of influencing factors and the results have emphasized the rebound effect of technological progress and industrial upgrades, whilst the development of clean energy can offset the increase in energy consumption thus contributing to the synergy of pollution and carbon reduction. Moreover, it has been highlighted that environmental background, industrial structure, and socio-economic characteristics of different cities should be considered comprehensively in order to better achieve the goals of "Beautiful China" and "Carbon Neutrality".

The spatial-temporal evolution mechanism of PM2.5 concentration based on China's climate zoning.

Increasing attention has been given to the impact of PM2.5 concentration on human health. Exploring the influential factors of PM2.5 is conducive to improving air quality. Most existing studies explore the factors that influence the PM2.5 concentration from the perspective of cities or urban agglomerations, while few studies are conducted from the perspective of climate zones. We used the standard deviation ellipse and spatial autocorrelation analysis to explore the spatial-temporal evolution of the PM2.5 concentration in different climate zones in China during 2000-2018. We used differentiated EKC to construct panel regression models to explore the differences in the influential factors of the PM2.5 concentration in three climate zones. The number of cities with PM2.5 concentration less than 35 µg/m3 increased in the different climate zones. The center of gravity of the PM2.5 concentration has remained at the junction of the temperate and subtropical monsoon climate zones. The PM2.5 concentration had a high positive spatial autocorrelation in the different climate zones. The high-high clustering areas were located in the south of the temperate monsoon climate zone and the north of the subtropical monsoon climate zone. There was an inverted "U-shaped" curve between the PM2.5 concentration and economic development in China that varied in different climate zones. Identifying the differences in the influential factors of PM2.5 concentration in different climate zones will help to accelerate the implementation of the EKC inflection point.

Effect of lockdown and associated mobility changes amid COVID-19 on air quality in the Kathmandu Valley, Nepal.

The COVID-19 pandemic caused a setback for Nepal, leading to nationwide lockdowns. The study analyzed the impact of lockdown on air quality during the first and second waves of the COVID-19 pandemic in the Kathmandu Valley. We analyzed 5 years of ground-based air quality monitoring data (2017-2021) from March to July and April to June for the first and second wave lockdowns, respectively. A significant decrease in PM2.5 (particulate matter with an aerodynamic diameter ≤ 2.5 µm) concentrations was observed during the lockdowns. The highest rate of decline in PM2.5 levels was observed during May and July compared to the pre-pandemic year. The PM2.5 concentration during the lockdown period remained within the WHO guideline limit and NAAQS for the maximum number of days compared to the lockdown window in the pre-pandemic years (2017-2019). Likewise, lower PM2.5 levels were observed during the second wave lockdown, which was characterized by a targeted lockdown approach (smart lockdown). We found a significant correlation of PM2.5 concentration with community mobility changes (i.e., walking, driving, and using public transport) from the Spearman correlation analysis. Lockdown measures restricted human mobility that led to a lowering of PM2.5 concentrations. Our findings can be helpful in developing urban air quality control measures and management strategies, especially during high pollution episodes.

Exploring the convergence patterns of PM2.5 in Chinese cities.

Economic development and ongoing urbanization are usually accompanied by severe haze pollution. Revealing the spatial and temporal evolution of haze pollution can provide a powerful tool for formulating sustainable development policies. Previous studies mostly discuss the differences in the level of PM2.5 among regions, but have paid little attention to the change rules of such differences and their clustering patterns over long periods. Therefore, from the perspective of club convergence, this study employs the log t regression test and club clustering algorithm proposed by Phillips and Sul (Econometrica 75(6):1771-1855, 2007. 10.1111/j.1468-0262.2007.00811.x) to empirically examine the convergence characteristics of PM2.5 concentrations in Chinese cities from 1998 to 2016. This study found that there was no evidence of full panel convergence, but supported one divergent group and eleven convergence clubs with large differences in mean PM2.5 concentrations and growth rates. The geographical distribution of these clubs showed significant spatial dependence. In addition, certain meteorological and socio-economic factors predominantly determined the convergence club for each city.

Deciphering urban traffic impacts on air quality by deep learning and emission inventory.

Air pollution is a major obstacle to future sustainability, and traffic pollution has become a large drag on the sustainable developments of future metropolises. Here, combined with the large volume of real-time monitoring data, we propose a deep learning model, iDeepAir, to predict surface-level PM2.5 concentration in Shanghai megacity and link with MEIC emission inventory creatively to decipher urban traffic impacts on air quality. Our model exhibits high-fidelity in reproducing pollutant concentrations and reduces the MAE from 25.355 µg/m3 to 12.283 µg/m3 compared with other models. And identifies the ranking of major factors, local meteorological conditions have become a nonnegligible factor. Layer-wise relevance propagation (LRP) is used here to enhance the interpretability of the model and we visualize and analyze the reasons for the different correlation between traffic density and PM2.5 concentration in various regions of Shanghai. Meanwhile, As the strict and effective industrial emission reduction measurements implementing in China, the contribution of urban traffic to PM2.5 formation calculated by combining MEIC emission inventory and LRP is gradually increasing from 18.03% in 2011 to 24.37% in 2017 in Shanghai, and the impact of traffic emissions would be ever-prominent in 2030 according to our prediction. We also infer that the promotion of vehicular electrification would achieve further alleviation of PM2.5 about 8.45% by 2030 gradually. These insights are of great significance to provide the decision-making basis for accurate and high-efficient traffic management and urban pollution control, and eventually benefit people's lives and high-quality sustainable developments of cities.

Global Meta-analysis of Airborne Bacterial Communities and Associations with Anthropogenic Activities.

Airborne microbiome alterations, an emerging global health concern, have been linked to anthropogenic activities in numerous studies. However, these studies have not reached a consensus. To reveal general trends, we conducted a meta-analysis using 3226 air samples from 42 studies, including 29 samples of our own. We found that samples in anthropogenic activity-related categories showed increased microbial diversity, increased relative abundance of pathogens, increased co-occurrence network complexity, and decreased positive edge proportions in the network compared with the natural environment category. Most of the above conclusions were confirmed using the samples we collected in a particular period with restricted anthropogenic activities. Additionally, unlike most previous studies, we used 15 human-production process factors to quantitatively describe anthropogenic activities. We found that microbial richness was positively correlated with fine particulate matter concentration, NH3 emissions, and agricultural land proportion and negatively correlated with the gross domestic product per capita. Airborne pathogens showed preferences for different factors, indicating potential health implications. SourceTracker analysis showed that the human body surface was a more likely source of airborne pathogens than other environments. Our results advance the understanding of relationships between anthropogenic activities and airborne bacteria and highlight the role of airborne pathogens in public health.

Estimates of PM2.5 concentrations spatiotemporal evolution across China considering aerosol components in the context of the Reform and Opening-up.

With astonishing and rapid development in China since the Reform and Opening-up in 1978, serious air pollution has become a great challenge. A better understanding of the response of PM2.5 pollution to socioeconomic development after the Reform and Opening-up policy is benefit for pollution control. However, heterogeneous influences of biophysical and socioeconomic activities on PM2.5 pollution pose great challenges in statistical simulation of PM2.5. Few statistical model regards aerosol species as the explanatory variables for heterogeneous formation mechanism to retrieve PM2.5 concentration. In this research, monthly PM2.5 concentration in China during 1980-2020 was reconstructed by a novel statistical strategy considering aerosol components (AC-RF). Three cross-validation (CV) methods, sample-based CV, spatial-based CV and temporal-based CV results indicated satisfactory performance of AC-RF model with correlation coefficient (R) of 0.92, 0.90, 0.86, respectively. A three-stage concluded on PM2.5 concentration annual variation in China was drawn as followed: Before 2000, PM2.5 level in China represented smooth evolution and mainly influenced by natural events with polluted region locating in Xinjiang province, North China and Central China. Since 2000, PM2.5 concentration increased to high level in the context of rapid socioeconomic development. Severe air pollution covered Jing-Jin-Ji agglomeration, Central China and Sichuan Basin. During 2012-2020, PM2.5 declined and polluted region shrank, which was benefited by the strictest-ever air pollution control measures. Based on aerosol components analysis, sulfate aerosol exhibited the most significant increase trend in recent 40 years and black aerosol variation is the most closely related to PM2.5 pollution. In conclusion, unsustainable development is the culprit for air quality deterioration. Strict and continuous air pollution control strategies are effective for air quality improvement.

A new combination model using decomposition ensemble framework and error correction technique for forecasting hourly PM2.5 concentration.

PM2.5 pollutants are seriously harmful to human health, which is of great significance for the forecasting of PM2.5 concentration. To accurately forecast hourly PM2.5 concentration, a new combination model based on agreement index variational mode decomposition (AIVMD), radial basis function neural network (RBF), induced ordered weighted averaging (IOWA) operator, long short-term memory neural network (LSTM) and error correction (EC), named AIVMD-RBF-IOWA-LSTM-EC, is proposed, which uses decomposition ensemble framework and error correction technique. Taking the reduction of reconstruction error in the process of VMD as the goal, an adaptive method to determine the mode number of VMD by agreement index (AI), named AIVMD, is proposed. Firstly, PM2.5 concentration data are decomposed into simple intrinsic mode function components (IMFs) by AIVMD to reduce the complexity of the data. Secondly, LSTM and RBF models are established for each IMF component, and the prediction results of each model are combined separately. Thirdly, an error correction model based on RBF is established to correct the prediction results. The predicted values of error are not only used to correct the prediction results, but also can be used as the induced value of IOWA operators to solve the weight allocation problem. Finally, the IOWA operator is used to weight the error correction prediction results, and the final result is obtained. To solve the problem that the forecasting accuracy of the combination model based on IOWA operators is low when the complementarity between single models is poor, a combination forecasting method with complementary disadvantage based on IOWA operators is proposed, which effectively improves the robustness of the model. A formula for calculating the proportion of complementary points is given. By solving the formula, the complementarity of the models can be judged, and the method of calculating the weight of the combined model can be selected accordingly. The proposed model is used to forecast PM2.5 concentration in Xi'an, and compared with the predicted results of contrast models. The results show that the proposed model has a great advantage in short-term forecasting of PM2.5 concentration.

Quantifying the drivers of PM2.5 variation in Shenyang, China: A factor decomposition model.

Taking variations in PM2.5 as indicators for assessing the performance of authority in air quality management will probably lead to misjudgment, as PM2.5 concentration is affected not only by anthropogenic emissions but also by uncontrollable circumstances. To solve this problem, we proposed a decomposition method to attribute the variations in PM2.5 to the contributions of meteorological conditions, cross-regional transports of pollutants, secondary aerosols, and local emissions. This method estimated the relationship between PM2.5 concentration and the various influencing factors using a semi-parametric generalized additive model. A case study was conducted in Shenyang, a heavily polluted city in northeast China, based on up to 595,000 hourly data samples from 2014 to 2017. The decomposition results indicated that the average PM2.5 in 2017 decreased by 39.80% compared with 2014, far exceeding the government's target of 15%, but only 11.79% of the decrease was benefited from the control of local emissions. The severe pollution event that occurred in November 2015 was induced by the combination of massive emissions from heating and meteorological conditions conducive to pollutant accumulation. Furthermore, the approach we proposed can be extended to any location that has monitoring data on air pollutant concentrations and meteorological conditions.

Impact of urban morphology on the spatial and temporal distribution of PM2.5 concentration: A numerical simulation with WRF/CMAQ model in Wuhan, China.

The urban morphology can significantly change the urban microclimate, which in turn affects the diffusion of air pollutants. Urban planning is the most important means of shaping urban morphology. Therefore, this study takes Wuhan as an example and uses the method of WRF/CMAQ coupled UCM model to analyze the spatial and temporal distribution characteristics of PM2.5 in the Wuhan metropolitan area in winter 2015. The six most important urban morphological indicators in urban planning: the floor area ratio and building height, building density and building width, vegetation coverage ratio, and urban fraction, are selected and classified into three groups. Studying their impact on the spatial and temporal distribution of PM2.5 concentration provides support for urban planners to improve air quality. The results show that the maximum value of PM2.5 concentration in Wuhan urban area occurs in the morning rush hour, and PM2.5 is distributed concentrically in the downtown of the city (within the second ring highway) according to the highways around the city. The PM2.5 concentration in the downtown area with the most extensive urban morphological index is the highest, and it decreases with increasing distance from the downtown. Among the six indicators, building density and urban fraction have the most significant impact on PM2.5 concentration because they have the greatest impact on the wind speed at 10 m. The height of the planetary boundary layer is the key factors affect the vertical and horizontal diffusion of air pollutants. Except for the vegetation coverage ratio, the increase of other urban morphological indicators will lead to a decrease of PM2.5 concentration in Wuhan urban area at night. During the daytime, increasing the floor area ratio and building height will cause an increasing of PM2.5 concentration, but other indicators have the opposite effects.

Spatio-temporal variation and daily prediction of PM2.5 concentration in world-class urban agglomerations of China.

The contradiction between the development of urban agglomerations and ecological protection has long been a challenging issue. China has experienced an astonishing expansion of its urban scale in the past 40 years, and nearly 783 million of the nation's people now live in cities. Beijing-Tianjin-Hebei, the Yangtze River Delta and the Pearl River Delta have been prioritized to become world-class clusters by 2020. The health effects of air pollution in these three urban agglomerations are becoming increasingly formidable. Given these conditions, using the daily mean PM2.5 concentration in 40 cities from January 2014 to December 2016, this research explored the spatial-temporal characteristics of PM2.5 concentrations in these three urban agglomerations. The annual mean PM2.5 concentrations in Beijing-Tianjin-Hebei, the Yangtze River Delta and the Pearl River Delta are 35.39 µg/m3, 53.72 µg/m3 and 78.54 µg/m3, respectively. Compared with the other two urban agglomerations, abundant rainfall causes the Pearl River Delta to have the lowest PM2.5 level. Furthermore, a general regression neural network (GRNN) method is developed to predict the PM2.5 concentration in these clusters on the second day, with inputs including the average, maximum and minimum temperature; average, maximum and minimum atmosphere; total rainfall; average humidity; average and maximum wind speed; and the PM2.5 concentration measured 1 day ahead. The results indicate that the GRNN method can precisely predict the concentration level in these clusters, and it is especially useful for the Pearl River Delta, as the underlying influence mechanism is more specified in this cluster than in the others. Importantly, this 1-day-ahead forecasting of PM2.5 concentrations can raise awareness among the public to improve their precautionary behaviours and help urban planners to provide corresponding support.

Combining machine learning models through multiple data division methods for PM2.5 forecasting in Northern Xinjiang, China.

In this study, daily average PM2.5 forecasting models were developed and applied in the Northern Xinjiang, China, through combining the back propagation artificial neural network (BPANN) and multiple linear regression (MLR) with another BPANN model. The meteorological (daily average precipitation, pressure, relative humidity, temperature, and wind speed, daily maximum wind speed and sunshine hours on the same day) and air pollutant data (daily PM2.5, PM10, SO2, CO, NO2, and O3 concentrations on the previous day) in January and August of each year from 2015 to 2019 were used as candidate inputs. The optimal member and combining models were evaluated through the leave-one-out cross-validation (LOOCV), fivefold cross-validation, and hold-out methods. Twelve member models with optimal or sub-optimal performance were further used to develop the combining models. The performances of the BPANN and MLR member models were different using three data division methods. The models were evaluated more comprehensively through the LOOCV. The performances of the combining models were generally better than the member models. For both member and combining models, the PM2.5 forecasting model performance in August was generally better than in January. The correlation coefficient (R) for the validation set of the optimal combination model was about 0.87 in January and 0.946 in August. These results showed that combining linear and nonlinear models through multiple data division methods would be an effective tool to forecast PM2.5 concentrations.

PM2.5 Concentration Estimation Based on Image Processing Schemes and Simple Linear Regression.

Fine aerosols with a diameter of less than 2.5 microns (PM2.5) have a significant negative impact on human health. However, their measurement devices or instruments are usually expensive and complicated operations are required, so a simple and effective way for measuring the PM2.5 concentration is needed. To relieve this problem, this paper attempts to provide an easy alternative approach to PM2.5 concentration estimation. The proposed approach is based on image processing schemes and a simple linear regression model. It uses images with a high and low PM2.5 concentration to obtain the difference between these images. The difference is applied to find the region with the greatest impact. The approach is described in two stages. First, a series of image processing schemes are employed to automatically select the region of interest (RoI) for PM2.5 concentration estimation. Through the selected RoI, a single feature is obtained. Second, by employing the single feature, a simple linear regression model is used and applied to PM2.5 concentration estimation. The proposed approach is verified by the real-world open data released by Taiwan's government. The proposed scheme is not expected to replace component analysis using physical or chemical techniques. We have tried to provide a cheaper and easier way to conduct PM2.5 estimation with an acceptable performance more efficiently. To achieve this, further work will be conducted and is summarized at the end of this paper.

Robust Confidence Intervals for PM2.5 Concentration Measurements in the Ecuadorian Park La Carolina.

In this article, robust confidence intervals for PM2.5 (particles with size less than or equal to 2.5 µm) concentration measurements performed in La Carolina Park, Quito, Ecuador, have been built. Different techniques have been applied for the construction of the confidence intervals, and routes around the park and through the middle of it have been used to build the confidence intervals and classify this urban park in accordance with categories established by the Quito air quality index. These intervals have been based on the following estimators: the mean and standard deviation, median and median absolute deviation, median and semi interquartile range, a-trimmed mean and Winsorized standard error of order a, location and scale estimators based on the Andrew's wave, biweight location and scale estimators, and estimators based on the bootstrap-t method. The results of the classification of the park and its surrounding streets showed that, in terms of air pollution by PM2.5, the park is not at caution levels. The results of the classification of the routes that were followed through the park and its surrounding streets showed that, in terms of air pollution by PM2.5, these routes are at either desirable, acceptable or caution levels. Therefore, this urban park is actually removing or attenuating unwanted PM2.5 concentration measurements.

PM2.5 concentration estimation using convolutional neural network and gradient boosting machine.

Surface monitoring, vertical atmospheric column observation, and simulation using chemical transportation models are three dominant approaches for perception of fine particles with diameters less than 2.5 micrometers (PM2.5) concentration. Here we explored an image-based methodology with a deep learning approach and machine learning approach to extend the ability on PM2.5 perception. Using 6976 images combined with daily weather conditions and hourly time data in Shanghai (2016), trained by hourly surface monitoring concentrations, an end-to-end model consisting of convolutional neural network and gradient boosting machine (GBM) was constructed. The mean absolute error, the root-mean-square error and the R-squared for PM2.5 concentration estimation using our proposed method is 3.56, 10.02, and 0.85 respectively. The transferability analysis showed that networks trained in Shanghai, fine-tuned with only 10% of images in other locations, achieved performances similar to ones from trained on data from target locations themselves. The sensitivity of different regions in the image to PM2.5 concentration was also quantified through the analysis of feature importance in GBM. All the required inputs in this study are commonly available, which greatly improved the accessibility of PM2.5 concentration for placed and period with no surface observation. And this study makes an exploratory attempt on pollution monitoring using graph theory and deep learning approach.

Robust Analysis of PM2.5 Concentration Measurements in the Ecuadorian Park La Carolina.

In this article, a robust statistical analysis of particulate matter (PM2.5) concentration measurements is carried out. Here, the region chosen for the study was the urban park La Carolina, which is one of the most important in Quito, Ecuador, and is located in the financial center of the city. This park is surrounded by avenues with high traffic, in which shopping centers, businesses, entertainment venues, and homes, among other things, can be found. Therefore, it is important to study air pollution in the region where this urban park is located, in order to contribute to the improvement of the quality of life in the area. The preliminary study presented in this article was focused on the robust estimation of both the central tendency and the dispersion of the PM2.5 concentration measurements carried out in the park and some surrounding streets. To this end, the following estimators were used: (i) for robust location estimation: α-trimmed mean, trimean, and median estimators; and (ii) for robust scale estimation: median absolute deviation, semi interquartile range, biweight midvariance, and estimators based on a subrange. In addition, nonparametric confidence intervals were established, and air pollution levels due to PM2.5 concentrations were classified according to categories established by the Quito Air Quality Index. According to these categories, the results of the analysis showed that neither the streets that border the park nor the park itself are at the Alert level. Finally, it can be said that La Carolina Park is fulfilling its function as an air pollution filter.

Socioeconomic factors and regional differences of PM2.5 health risks in China.

China is a country with one of the highest concentrations of airborne particulate matter smaller than 2.5 µm (PM2.5) in the world, and it has obvious spatial-distribution characteristics. Areas of concentrated population tend to be regions with higher PM2.5 concentrations, which further aggravate the impact of PM2.5 pollution on population health. Using PM2.5-concentration and socioeconomic data for 225 cities in China in 2015, we adopted a PM2.5-health-risk-assessment method (with simplified calculation) and applied the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model to analyze the effects of socioeconomic factors on PM2.5 health risks. The results showed that: (1) At the national level, the order of contribution degree of each socioeconomic factor in the PM2.5-health-risk and PM2.5-concentration model is consistent. (2) From a regional perspective, in all three regions, the industrial structure is the decisive factor affecting PM2.5 health risks, and reduction of energy intensity increases PM2.5 health risks, but the impact of the total amount of urban central heating on PM2.5 health risks is very low. In the eastern region, the increased urbanization rate and length of highways significantly increase PM2.5 health risks, but the increasing effect of the extent of built-up area is the lowest. In the central region, the increasing effects of the extent of built-up area on PM2.5 health risks are significantly greater than the decreasing effects of the urbanization rate. In the western region, economic development has the least effect on reducing PM2.5 health risks. Our research enriches PM2.5-health-risk theory and provides some theoretical support for PM2.5-health-risk diversity management in China.

Impact of air pollution control policies on future PM2.5 concentrations and their source contributions in China.

To investigate the impact of air pollutant control policies on future PM2.5 concentrations and their source contributions in China, we developed four future scenarios for 2030 based on a 2013 emission inventory, and conducted air quality simulations for each scenario using the chemical transport model GEOS-Chem (version 9.1.3). Two energy scenarios i.e., current legislation (CLE) and with additional measures (WAM), were developed to project future energy consumption, reflecting, respectively, existing legislation and implementation status as of the end of 2012, and new energy-saving policies that would be released and enforced more stringently. Two end-of-pipe control strategies, i.e., current control technologies (until 2017) and more stringent control technologies (until 2030), were also developed. The combinations of energy scenarios and end-of-pipe control strategies constitute four emission scenarios (2017-CLE, 2030-CLE, 2017-WAM, and 2030-WAM) evaluated in simulations. PM2.5 concentrations at national level were estimated to be 57 µg/m3 in the base year 2013, and 58 µg/m3, 42 µg/m3, 42 µg/m3, and 30 µg/m3 under the 2017-CLE, 2030-CLE, 2017-WAM, and 2030-WAM scenarios in 2030, respectively. Large PM2.5 reductions between 2013 and 2030 were estimated for heavily polluted regions (Sichuan Basin, Middle Yangtze River, North China). The energy-saving policies show similar effects to the end-of-pipe emission control measures, but the relative importance of these two groups of policies varies in different regions. Absolute contributions to PM2.5 concentrations from most major sources declined from 2017-CLE to 2030-WAM. With respect to fractional contributions, most coal-burning sectors (including power plant, industrial and residential coal burning) increased from 2017-CLE to 2030-WAM, due to larger reductions from non-coal sources, including transportation and biomass open burning. Residential combustion and open burning had much lower fractional contribution to ambient PM2.5 concentrations in the 2017-WAM/2030-WAM compared to the 2017-CLE/2030-CLE scenarios. Fractional contributions from transportation were reduced dramatically in 2030-CLE and 2030-WAM compared to 2017-CLE/2017-WAM, due to the enforcement of stringent end-of-pipe emission controls. Across all scenarios, coal combustion remained the single largest contributor to PM2.5 concentrations in 2030. Reducing PM2.5 emissions from coal combustion remains a strategic priority for air quality management in China.