With frequent severe haze and smog episodes in Chinese cities, an increasing number of studies have focused on estimating the impact of fine particulate matter (PM2.5) on public health. However, the ...current use of national and provincial demographic data might mask regional differences and lead to inaccurate estimations of pollution-related health impacts across cities. We applied the Global Burden of Disease methodology to develop a dataset of premature deaths attributed to ambient PM2.5 in 129 Chinese cities in 2006, 2010 and 2015, based on the information of baseline mortality rates and population densities at the city level. Our results suggested that ambient PM2.5 pollution led to 631,230 (95% confidence interval: 281,460–873,800) premature deaths in those cities in 2015, which was similar to that in 2010, but 42.8% higher than that in 2006. The reduction of premature deaths was not as obvious as the improvement in air quality in recent years, primarily owing to the aging Chinese population. For large and medium/small cities, the effects of PM2.5 abatement on alleviating public health burdens were lower than those for megalopolises and metropolises; however, such large and medium/small cities are at risk of increasing future PM2.5 pollution levels due to rapid development. Significant differences in PM2.5-induced premature deaths indicated the need for specific policies to mitigate the health burden of air pollution in different types of Chinese cities.
•Health burdens of PM2.5 in 129 Chinese cities during 2006-2015 were calculated.•Ageing population hinders improvement of health benefit from pollution control.•Characteristics of health burden for different types of cities were analyzed.•Regional characteristics should be considered when developing policies.
A multiscale analysis of meteorological trends was carried out to investigate the impacts of the large-scale circulation types as well as the local-scale key weather elements on the complex air ...pollutants, i.e., PM2.5 and O3 in China. Following an accompanying paper on synoptic circulation impact (Gong et al., 2022), using a multi-linear regression model, the trends of key meteorological elements at local scale, i.e., temperature, relative humidity, solar radiation, PBL height, precipitation and wind speed, are analyzed and correlated with the trends of PM2.5 and O3 levels to identify significantly influencing factors in seven Chinese cities. Furthermore, with additional emission surrogates introduced in the regression model, the impacts on the trends by meteorology and emission were separated and quantified. Results show that the increasing trends of O3 at most Chinese cities were largely attributed to the trends of meteorological elements of temperature and solar radiation, while the trends of PM2.5 are mostly contributed by the emission reduction measures of PM2.5 and its precursors. The meteorology alone can explain approximately 57–80% of the O3 variations and only 20–33% of the PM2.5 variations. With the addition of emission surrogates, this explanation percentage is increased to about 57–82% for O3 but significantly enhanced to 71–83% for PM2.5.
Trend of O3 and associated trends of impacting elements. T and Rad are from CMA observations and emissions are from MEIC Display omitted
•The association of key weather elements with PM2.5 and O3 trends in China.•The identification of the impact similarity and difference of weather elements on PM2.5 and O3 for three regions.•The quantification of separate contributions of meteorology and emissions to the PM2.5 and O3 trends.
Estimation of hourly and continuous ground-level fine particulate matter (PM2.5) concentrations is essential for PM2.5 pollution sources identifications, targeted policy development and population ...exposure research. However, current PM2.5 estimation studies rely heavily on satellite-based aerosol optical depth (AOD) data, and the limited transit times of polar-orbiting satellites such as Terra and Aqua, nighttime gaps in data from geostationary satellites such as Himawari-8, and cloud contamination reported for both types of satellites challenge the estimation of spatiotemporally continuous PM2.5 concentrations. In this study, spatiotemporal PM2.5 characteristic was constructed by the spatiotemporal fusion method. Specifically, multi-source data, including spatiotemporal, periodic, meteorological, vegetation, anthropogenic and topological characteristics, were incorporated into an ensemble learning method that combined extreme gradient boosting (XGBoost), k-nearest neighbour (KNN) and back-propagation neural network (BPNN) algorithms in level 1 and used linear regression (LR) for integration in level 2. The optimized stacking strategy that considered PM2.5 spatiotemporal autocorrelation was called the ST-stacking model. The model was trained, validated and tested with data acquired for China in 2017. The ST-stacking model outperformed XGBoost, KNN and BPNN models by 9.27% on average, with an R2 = 0.9191. Using the model, the 24-h and continuous ground-level PM2.5 concentrations in mainland China on 11 May 2017 were mapped, and parts of Beijing and Chengdu were selected for more detailed analysis. The PM2.5 concentrations in Taklimakan Desert, North China Plain, Sichuan Basin and Yangtze Plain were much higher than those in other locations on this day, which was generally consistent with the long-term patterns reported in previous studies.
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•Spatiotemporal autocorrelation is considered for PM2.5 relationship reconstruction.•The ensemble learning method is established to improve robustness and accuracy.•The hourly and continuous PM2.5 concentrations in China on 11 May 2017 are mapped.•ST-stacking model outperforms the individual models, with R2 = 0.9191.•Spatiotemporal characteristic contributes the most to model performance.
As the fine particulate matter (PM2.5) polluting seriously threat people's health, exploring its mitigation strategies has become an urgent issue to be studied. Urban land use, the carrier of urban ...functions and human activities, has been widely recognized as an important contributor of PM2.5 pollution. Taking Wuhan metropolitan area as an example, this study employs a deep learning simulation method to explore the effects of land use types and density on the spatial distribution of PM2.5 pollutants. The PM2.5 concentration data, raster-based land use data and meteorological conditions data are analyzed to identify their dynamic spatiotemporal characteristics. The meteorological conditions, including temperature and wind speed, are incorporated into the simulation platform, which improves the precision significantly. The simulation results show that PM2.5 concentration caused by construction land such as industrial, residential, transportation, logistics and warehousing, commercial, utilities, and public service sequentially decreases. The impact of FAR on PM2.5 concentration is spatially different. With the increase of FAR, some north construction pixels present PM2.5 mitigation effects while a few grids in the south appear heavier pollution. By analyzing the results of different simulation scenarios, specific spatial-based PM2.5 mitigation strategies and control measures are provided to promote healthy and sustainable urban development. This method can be transferred and applied to other metropolitans, so as to provide as a reference for policymakers and urban planners to promote effective air pollution mitigation strategies from the view of spatial planning.
•Explore the impact of construction lands on PM2.5 using a deep learning approach.•Urban meteorological conditions are considered in the model.•Effects order of lands on PM2.5: Industrial > residential > transportation.•The increase of FAR alleviates the PM2.5 pollution in most north pixels.•Spatial-based PM2.5 mitigation strategies were proposed.
Achieving carbon neutrality before 2060 newly announced in China are expected to substantially affect air quality. Here we project the pollutants emissions in China based on a carbon neutrality ...roadmap and clean air policies evolution; national and regional PM2.5 and O3 concentrations in 2030 (the target year of carbon peak), 2035 (the target year of “Beautiful China 2035” launched by the Chinese government to fundamentally improve air quality) and 2060 (the target year of carbon neutrality) are then simulated using an air quality model. Results showed that compared with 2019, emissions of SO2, NOx, primary PM2.5, and VOCs are projected to reduce by 42%, 42%, 44%, and 28% in 2030, by 57%, 58%, 60%, and 42% in 2035, by 93%, 93%, 90% and 61% in 2060 respectively. Consequently, in 2030, 2035, and 2060, the national annual mean PM2.5 will be 27, 23, and 11 μg m−3; and the 90th percentile of daily 8-h maxima of O3 (O3-8h 90th) will be 129, 123, and 93 μg m−3; 82%, 94%, and 100% of 337 municipal cities will reach the current national air quality standard, respectively. It's expected that the “Beautiful China 2035” target is very likely to be achieved, and about half of the 337 cities will meet the current WHO air quality guideline in 2060. In the near future, strict environmental policies driven by “Beautiful China 2035” are needed due to their substantial contribution to emission reductions. By 2060, the low-carbon policies driven by the carbon neutrality target are expected to contribute to larger than 80% of reductions in PM2.5 and O3-8h 90th concentrations relative to the 2020 levels, implying that more attention could be paid to low-carbon policies after 2035. Our research would provide implications for future co-governance of air pollution and climate change mitigation in China and other developing countries.
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•The air quality cobenefits of achieving China's carbon neutrality before 2060 was quantitatively investigated.•This study proposed a mid-to-long-term air quality improvement pathway until 2060 of China (CAEP-CAP).•End-of-pipe controls can greatly improve air quality before 2035, while low-carbon policy will be more critical after 2035.
Understanding spatiotemporal variation of PM1 (mass concentrations of particles with aerodynamic diameter < 1 μm) is important due to its adverse effects on health, which is potentially more severe ...for its deeper penetrating capability into human bodies compared with larger particles. This study aimed to quantify the spatial and temporal distribution of PM1 across China as well as its ratio with PM2.5 (<2.5 μm) and relationships with meteorological parameters in order to deepen our knowledge of the drivers of air pollution in China. Ground-based monitoring PM1 and PM2.5 measurements, along with collocated meteorological data, were obtained from 96 stations in China for the period from November 2013 to December 2014. Generalized additive models were employed to examine the relationships between PM1 and meteorological parameters. We showed that PM1 concentrations were the lowest in summer and the highest in winter. Across China, the PM1/PM2.5 ratios ranged from 0.75–0.88, reaching higher levels in January and lower in August. For spatial distribution, higher PM1/PM2.5 ratios (>0.9) were observed in North-Eastern China, North China Plain, coastal areas of Eastern China and Sichuan Basin while lower ratios (<0.7) were present in remote areas in North-Western and Northern China (e.g., Xinjiang, Tibet and Inner Mongolia). Higher PM1/PM2.5 ratios were observed on heavily polluted days and lower ratios on clean days. The high PM1/PM2.5 ratios observed in China suggest that smaller particles, PM1 fraction, are key drivers of air pollution, and that they effectively account for the majority of PM2.5 concentrations. This emphasised the role of combustion process and secondary particle formation, the sources of PM1, and the significance of controlling them.
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•Across China, the PM1/PM2.5 ratios ranged from 0.75 in August to 0.88 in January.•High PM1/PM2.5 ratios (>0.9) were observed in North-Eastern and Central China.•Low PM1/PM2.5 ratios (<0.7) were present Western China and Inner Mongolia.•Overall, PM1/PM2.5 ratios in China are higher than those elsewhere in the world.
Smaller particles, PM1 fraction, are key drivers of air pollution in China accounting for the majority of PM2.5 concentrations.
Previous studies have predominantly focused on developing high spatiotemporal resolution PM2.5 models utilizing moderate-resolution imaging spectroradiometer (MODIS) aerosol optical depth (AOD) ...products alongside certain meteorological factors. However, MODIS AOD is not continuous, and there is a problem of missing data, which is not conducive to the study of PM2.5 in areas without AOD. This study proposes a new method for estimating PM2.5 concentration in North China, which not only simplifies the complex process associated with the use of AOD, but also partially solves the issue of missing AOD data. This study proposes a machine learning (random forest, RF; back propagation neural network, BPNN) based method for estimating PM2.5 using meteorological parameters obtained from the fifth-generation reanalysis (ERA5) dataset released by the European Centre for Medium-Range Weather Forecasts and considering the effects of land cover type, digital elevation model, vegetation index and population data. The model performed well, with 10-fold cross-validation (coefficient of determination) R2 and (root-mean-square error) RMSE of 0.79/0.95 and 26.10/13.22 µg/m3, respectively, for BPNN and RF. The estimated hourly performance of the RF model in winter (00:00 to 23:00 BST) with an R2 ranging from 0.92 to 0.96, an RMSE of 11.45 to 16.70 µg/m3. RF model with the best performance and ERA5 were selected to build a high-resolution (0.25°×0.25°) hourly PM2.5 map (PMM), and the PMM was compared with CHAP, with R2 and RMSE of 0.75 and 20.62 µg/m3, respectively. This study further investigates the impacts of land cover types, digital elevation model, and land surface characteristics on the spatial distribution of PM2.5 in North China.
High PM2.5 episodes frequently occur in Northeast Asia, and the source−receptor relationship for PM2.5 in megacities is a critical issue. As the largest industrial city in South Korea, Ulsan suffers ...from frequent high PM2.5 episodes. However, studies on the long-range atmospheric transport (LRAT), local emissions, and secondary formation of PM2.5 in Ulsan have been limited. In this study, the characteristics of high PM2.5 episodes in Ulsan were interpreted using hourly data for PM2.5 components. The periods with the highest PM2.5 concentrations in winter 2014 (February 24–26; 99.3 ± 18.6 μg/m3) and summer 2014 (June 24–27; 49.9 ± 12.3 μg/m3) were designated as Pollution Periods 1 and 2, respectively. In general, secondary inorganic ions (SO42−, NO3−, and NH4+; SNA) were generated by the liquid phase reaction of water-soluble materials during winter, and sulfate and secondary organic aerosols were mainly formed via photochemical reactions during summer. During Pollution Period 1, the concentrations of sulfate, organic carbon, and elemental carbon sharply increased, and three major sources were identified: (1) LRAT from fossil fuel and biomass burning in eastern China and North Korea, (2) the influence of petrochemical and non-ferrous industrial facilities in Ulsan, and (3) enhanced secondary formation of ammonium sulfate and organic aerosols due to air stagnation. During Pollution Period 2, the concentration of SNA and heavy metals sharply increased, and three pollution sources were identified: (1) the influence of local industrial facilities and ship emissions, (2) external inflow from thermal power stations and national industrial facilities in southern coastal cities, and (3) secondary organic and inorganic formation. In this study, the reasons for the high winter and summertime PM2.5 events in Ulsan were more clearly understood, which can be the basis for the establishment of PM2.5 management policies that consider LRAT, local primary emissions, and secondary formation.
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•PM2.5 components were measured semi-continuously in the industrial city of Ulsan.•The reason for high PM2.5 events was investigated using monitoring and modeling data.•In winter, LRAT and air stagnation were major reasons for the high PM event.•The high PM event in summer was due to local industry and secondary formation.
Environmental degradation is significantly studied both in the past and the current literature; however, steps towards reducing the environmental pollution in carbon emission and haze pollution like ...PM2.5 are not under rational attention. This study tries to cover this gap while considering the carbon emission and PM2.5 through observing the role of renewable energy, non-renewable energy, environmental taxes, and ecological innovation for the top Asian economies from 1990 to 2017. For analysis purposes, this research considers cross-sectional dependence analysis, unit root test with and without structural break (Pesaran, 2007), slope heterogeneity analysis, Westerlund and Edgerton (2008) panel cointegration analysis, Banerjee and Carrion-i-Silvestre (2017) cointegration analysis, long-short run CS-ARDL results, as well as AMG and CCEMG for robustness check. The empirical evidence in both the short- and long-run has confirmed the negative and significant effect of renewable energy sources, ecological innovation, and environmental taxes on carbon emissions and PM2.5. Whereas, non-renewable energy sources are causing environmental degradation in the targeted economies. Finally, various policy implications related to carbon emission and haze pollution like PM2.5 are also provided to control their harmful effect on the natural environment.
•Renewable energy sources, ecological innovation, and environmental taxes negatively effect on carbon emissions and PM2.5.•Non-renewable energy sources are causing environmental degradation in the form of carbon emission.•Renewable energy sources reduce the higher level of carbon emissions in the targeted economies.•Renewable energy, ecological innovation, and environmental taxes positively impacted the environment in Asian countries.
In recent years, winter PM2.5 and summer O3 pollution which often occurred with air stagnation condition has become a major concern in China. Thus, it is imperative to understand the air stagnation ...distribution in China and elucidate its impact on air pollution. In this study, three air stagnation indices were calculated according to atmospheric thermal and dynamics parameters using ERA5 data. Two improved indices were more suitable in China, and they displayed similar characteristics: most of the air stagnant days were found in winter, and seasonal distributions showed substantial regional heterogeneity. During stagnation events, flat west or northwest winds at 500 hPa and high pressure at surface dominated, with high relative humidity (RH) and temperature (T), weak winds in most regions. The pollutants concentrations on stagnant days were higher than those on non-stagnant days in most studied areas, with the largest difference of the 90th percentiles of maximum daily 8-h average (MDA8) O3 up to 62.2 μg m−3 in Pearl River Delta (PRD) and PM2.5 up to 95.8 μg m−3 in North China Plain (NCP). During the evolution of stagnation events, the MDA8 O3 concentrations showed a significant increase (6.0 μg m−3 day−1) in PRD and a slight rise in other regions; the PM2.5 concentrations and the frequency of extreme PM2.5 days increased, especially in NCP. Furthermore, O3 was simultaneously controlled by temperature and stagnation except for Xinjiang (XJ), with the average growth rate of 19.5 μg m−3 every 3 °C at 19 °C–31 °C. PM2.5 was dominated by RH and stagnation in northern China while mainly controlled by stagnation in southern China. Notably, the extremes of summer O3 (winter PM2.5) pollution was most associated with air stagnation and T at 25 °C–31 °C (air stagnation and RH >50%). The results are expected to provide important reference information for air pollution control in China.
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•Comparison of the three air stagnation indices in China.•Highest frequency of stagnant days in winter with substantial regional heterogeneity•O3 and PM2.5 increase by 12.0% and 16.8% on stagnant days on average.•Higher growth rate with the occurrence of stagnation for O3 (PM2.5) as temperature (humidity) increased•More extreme pollution under stagnation, temperature within 25–31 °C or humidity above 50%
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