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Low-carbon cities have become a new trend in regional development around the world. Whether they can improve the environment in China, especially the air quality, remains to be tested. In this paper we take low-carbon city construction as a quasi-natural experiment and empirically test the net effects, influencing factors, and dynamic effects of low-carbon city construction on air quality by constructing a multistage propensity score matching and Difference-in-Differences model. After a series of robustness tests, the following conclusions are drawn: first, low-carbon city construction reduces the regional Air Quality Index, inhalable particulate matter, fine particulate matter, and NO2 concentrations. Among them, the construction effect in 2017 was the most significant. Therefore, it is necessary to continue to promote low-carbon city policies and accurately identify different types of air pollutants to improve the overall effectiveness of low-carbon city policies. Second, temperature, humidity, wind level, and other meteorological factors, as well as gross domestic product for the proportion of secondary industry, will affect air quality. Therefore, it is necessary to comprehensively consider meteorological, economic, social, and other influencing factors in an early stage of the construction of the next batch of low-carbon cities, so as to avoid falling into the trap of "building first and managing later". Third, the impact of secondary industry on air quality is significantly greater than that of tertiary industry. Therefore, the upgrading of industrial structure promoted by low-carbon city policy is effective in improving air quality. Fourth, the construction of low-carbon cities in western China has the most significant impact on air quality improvement. Therefore, the joint prevention and control mechanism of air pollution control in urban agglomeration should be established. [ABSTRACT FROM AUTHOR]

In September 2018, China's air quality monitoring protocol was amended from the standard conditions to actual conditions for particulate matter and to reference conditions for gaseous pollutants. Due to the amendment, the reported concentrations of the gaseous pollutants decreased by a constant rate of 8.4%, and the averages of PM2.5 (particulate matter that has an aerodynamic diameter of 2.5 microns or smaller) reported during September 2017 and August 2018 decreased by 7.9 ± 6.1% at 99% of the monitoring stations. Comparing the periods before and after the amendment, the 12-month PM2.5 concentrations at 17.2% of the stations actually increased despite appearing to decrease if the amendments were not considered. We reviewed 370 papers published in 2020 that utilized this air quality dataset, and 21% of these papers used the data before and after the amendment without explicitly stating whether or how conversions were conducted. It is urgent to widely broadcast the protocol amendment to ensure proper use of this extensively cited dataset. [ABSTRACT FROM AUTHOR]

Growing evidence links intra-urban gradients in atmospheric fine particles (PM2.5), a complex and variable cocktail of toxic chemicals, to adverse health outcomes. Here, we propose an improved hierarchical deep learning model framework to estimate the hourly variation of PM2.5 mass concentration at the street level. By using a full-year monitoring data (including meteorological parameters, hourly concentrations of PM2.5, and gaseous precursors) from multiple stations in Shanghai, the largest city in China, as a training dataset, we first apply a convolutional neural network to obtain cross-domain and time-series features so that the inherent features of air quality and meteorological data associated with PM2.5 can be effectively extracted. Next, a Gaussian weight calculation layer is used to determine the potential interaction effects between different regions and neighboring stations. Finally, a long and short-term memory model layer is used to efficiently extract the temporal evolution characteristics of PM2.5 concentrations from the previous output layer. Further comparative analysis reveals that our proposed model framework significantly outperforms previous benchmark methods in terms of the stability and accuracy of PM2.5 prediction, which has important implications for the intra-urban health assessment of PM2.5-related pollution exposures. [ABSTRACT FROM AUTHOR]

Dust storms are of great importance to climate change, air quality, and human health. In this study, a complete application frame of integrating hybrid methods based on multi-source data is proposed for remote sensing monitoring and process analysis of dust storms. In the frame, horizontal spatial distribution of dust intensity can be mapped by optical remote sensing products such as aerosol optical depth (AOD) from MODIS; the vertical spatial distribution of dust intensity by LIDAR satellite remote sensing products such as AOD profile from CALIPSO; geostationary satellite remote sensing products such as Chinese Fengyun or Japanese Himawari can achieve high-frequency temporal distribution information of dust storms. More detailed process analysis of dust storms includes air quality analysis supported by particulate matter (PM) data from ground stations and the dust emission trace and transport pathways from HYSPLIT back trajectory driven by meteorological data from the Global Data Assimilation System (GDAS). The dust storm outbreak condition of the source location can be proved by precipitation data from the WMO and soil moisture data from remote sensing products, which can be used to verify the deduced emission trace from HYSPLIT. The proposed application frame of integrating hybrid methods was applied to monitor and analyze a very heavy dust storm that occurred in northern China from 14–18 March 2021, which was one of the most severe dust storms in recent decades. Results showed that the dust storm event could be well monitored and analyzed dynamically. It was found that the dust originated in western Mongolia and northwestern China and was then transmitted along the northwest–southeast direction, consequently affected the air quality of most cities of northern China. The results are consistent with the prior research and showed the excellent potential of the integration of the hybrid methods in monitoring dust storms. [ABSTRACT FROM AUTHOR]

In this paper, the Weather Research Forecast (WRF) Comprehensive Air Quality Model with Extensions (CAMx) modeling system with the particulate source apportionment technology (PSAT) module was used to study and analyze the spatial and temporal distribution of atmospheric pollutant concentrations and the source apportionment of fine particles (PM2.5) under the base year and an emission reduction scenario in the Shandong province, China. Our results show that industry is the largest contributor of PM2.5. In addition, the contribution of key energy-related industries was as high as 29.5%, with the thermal power industry being the largest individual contributor. In January, the largest contribution came from residents, reaching 41.3%. Moreover, loose coal burning in rural areas contributed up to 19.4% in winter. Our results also show that the emission reduction scenario had palpable effects on the reduction of air pollution. The more the emissions of SO2, NOX, PM2.5, and PM10 were reduced, the more the average concentration was decreased. The implementation of energy conservation and emission reduction policies by industry and resident is conducive to improving the quality of the atmospheric environment. In particular, a comprehensive control of loose coal burning in winter could significantly improve heavy pollution by particulate matter in winter. [ABSTRACT FROM AUTHOR]

Biomass burning (BB) plays an important role in the formation of heavy pollution events during harvest seasons in the Beijing–Tianjin–Hebei (BTH) region by releasing trace gases and particulate matter into the atmosphere. A better understanding of spatial-temporal variations of BB in BTH is required to assess its impacts on air quality, especially on heavy haze pollution. The fourth version of the Global Fire Emissions Database (GFED4)'s fire counts and carbon emissions data were used in this research, which shows the varying number of fire counts in China from 2003 to 2020 demonstrated a fluctuating but generally rising trend, with a peak in 2013. Most fire counts were concentrated in three key periods: March (11%), June–July (33%), and October (9.68%). The increase in fire counts will inevitably lead to the growth of carbon emissions. The four major vegetation types of the fires were agriculture (58.1%), followed by grassland (35.5%), and forest (4.1%), with the fewest in peat. In addition, a separate study for the year 2020 found that the fire counts and carbon emissions were different for this year, with the overall average trend in the study time. For example, the monthly peak fire counts changed from June to March. The cumulative emissions of carbon, CO, CO2, CH4, dry matter, and particulate matter from BB in BTH reached 201 Gg, 39 Gg, 670 Gg, 2 Gg, 417 Gg, and 3 Gg in 2020, respectively. [ABSTRACT FROM AUTHOR]

In recent years, air pollution has become a serious threat, causing adverse health effects and millions of premature deaths in China. This study examines the spatial-temporal characteristics of ambient air quality in five provinces (Shaanxi (SN), Xinjiang (XJ), Gansu (GS), Ningxia (NX), and Qinghai (QH)) of northwest China (NWC) from January 2015 to December 2018. For this purpose, surface-level aerosol pollutants, including particulate matter (PMx, x = 2.5 and 10) and gaseous pollutants (sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3)) were obtained from China National Environmental Monitoring Center (CNEMC). The results showed that fine particulate matter (PM2.5), coarse particulate matter (PM10), SO2, NO2, and CO decreased by 28.2%, 32.7%, 41.9%, 6.2%, and 27.3%, respectively, while O3 increased by 3.96% in NWC during 2018 as compared with 2015. The particulate matter (PM2.5 and PM10) levels exceeded the Chinese Ambient Air Quality Standards (CAAQS) Grade II standards as well as the WHO recommended Air Quality Guidelines, while SO2 and NO2 complied with the CAAQS Grade II standards in NWC. In addition, the average air quality index (AQI), calculated from ground-based data, improved by 21.3%, the proportion of air quality Class I (0–50) improved by 114.1%, and the number of pollution days decreased by 61.8% in NWC. All the pollutants' (except ozone) AQI and PM2.5/PM10 ratios showed the highest pollution levels in winter and lowest in summer. AQI was strongly positively correlated with PM2.5, PM10, SO2, NO2, and CO, while negatively correlated with O3. PM10 was the primary pollutant, followed by O3, PM2.5, NO2, CO, and SO2, with different spatial and temporal variations. The proportion of days with PM2.5, PM10, SO2, and CO as the primary pollutants decreased but increased for NO2 and O3. This study provides useful information and a valuable reference for future research on air quality in northwest China. [ABSTRACT FROM AUTHOR]

A year-long study was performed to comprehensively investigate the mass concentration, water-soluble ions, organic and elemental carbons, and elemental components of atmospheric fine particles from March 2006 to February 2007 in Qingdao. The results showed that PM2.5 concentration was over twice the National Ambient Air Quality Standards of 35 µg/m³ (GB3095-2012), with the highest value in winter (110 µg/m³) and the lowest in summer (42.8 µg/m³). Non-sea salt-SO42- (nss-SO42-), NO3-, and NH4+ were the dominant water-soluble ions in PM2.5, and showed the highest concentrations in autumn, winter and autumn, respectively, and all had the lowest concentrations in summer. Organic carbon (OC) and elemental carbon (EC) had the lowest levels in summer and the highest values in winter and autumn, respectively. The lower acetic/formic acid (A/F) ratio in summer suggested a predominant secondary formation of water-soluble organic acids. Cl-depletion mainly occurred in summer, along with the largest sea salt proportion in particles in a year when winds came from the southeastern sea. The enrichment factors (EFs) of elements followed an increasing order: Si, Al, Ti, Mg, Ca, Fe, Na, Sr, K, Mn, Ba, V, Cr, Ni, Co, Sc, Cu, As, Cl, Zn, Pb, S, Br, Cd, and Se. The higher EFs from 10.1 to 10,487 for Sr, K, Mn, Ba, V, Cr, Ni, Co, Sc, Cu, Cl, Zn, Pb, S, Br, Cd, and Se suggested the significant influence of anthropogenic sources in the area. Mass closure showed that (NH4)2SO4, organic matter, and NH4NO3 were the main components, and contributed 31.0%, 25.2%, and 14.0% of PM2.5 mass in Qingdao, respectively. Source apportionment results showed that secondary sulfate, nitrate, and coal combustion were the main sources. Based on this study and previous publications, the PM2.5 characteristics--including seasonality in mass and chemical species concentrations and Cl-depletion in summer--were obtained across coastal cities in China. [ABSTRACT FROM AUTHOR]

A survey was conducted to effectively investigate the characteristics of airborne particulate pollutants PM10 and PM2.5 during the peak tourist season at Mount Wutai Buddhism scenic spot, Shanxi, China. Characteristics of the PM10 and PM2.5 in Wu Ye Temple (core incense burners), Manjusri Temple (a traffic hub), Yang Bai Lin Village (a residential district), and Nan Shan Temple (located in a primitive forest district), were determined. The results showed that the PM10 concentration was more than 1.01-1.14 times higher than the threshold (50 μg/m³) of World Health Organization Air Quality Guidelines (2005), and the PM2.5 concentration was 1.75-2.70 times higher than the above standard (25 μg/m³). Particle size analysis indicated that the distribution of fine particulate matter in Wu Ye Temple ranged from 0 to 3.30 μm. In other sampling points, the fine particulate was mainly distributed in the range of 0-5.90 μm. The particulates in Wu Ye Temple were mainly characterized by spherical, rod-like, and irregular soot aggregates (PM10) and spherical particles of dust (PM2.5). Manjusri Temple and Yang Bai Lin Village predominantly exhibited irregular soil mineral particulate matter (PM10), and amorphous ultrafine soot particulate matter (PM2.5). [ABSTRACT FROM AUTHOR]

With the rapid increase of the vehicle population in the Guanzhong Plain (GZP), the fine particulate matter (PM2.5) emitted by vehicles has an impact on regional air quality and public health. The spatial distribution of primary and secondary PM2.5 concentrations from vehicles in GZP in January and July 2017 was simulated in this study by using the Weather Research and Forecasting (WRF) model and the California Puff (CALPUFF) air quality model. The contributions of vehicle-related emission sources to total PM2.5 concentrations were also calculated. The results show that although the emissions of primary PM2.5, NOx, and SO2 in July were greater than those in January, the hourly average concentrations of primary and secondary PM2.5 in January were significantly higher than those in July. The highest concentrations of primary and total PM2.5 were mostly located in the urban areas of Xi'an and Xianyang in the central region of GZP. The contributions of exhaust emissions, secondary nitrates, brake wear, tire wear, and secondary sulfate to the total PM2.5 concentrations in GZP were 50.37%, 34.76%, 10.79%, 4.06%, and 0.04% in January and 71.91%, 11.14%, 11.89%, 5.03%, and 0.03% in July, respectively. These results will help us to further control PM2.5 pollution caused by vehicles. [ABSTRACT FROM AUTHOR]

The unique energy structure, high intensity of coal production, and complex terrain, make Fenwei Plain a highly polluted region in China. In this study, we characterized the transport characteristic and sources of PM2.5 (the fraction of particulate matter ≤ 2.5 μm) in Sanmenxia, a polluted city in canyon terrain. The results showed that special topography in Sanmenxia had an important role in the transport of particulates. Sanmenxia is located between two northeast-southwest facing mountains, showing a special local circulation. The local circulation was dominated by a downslope wind at nighttime, while the cross−mountain airflow and zonal wind were dominant during the daytime in the canyon terrain. PM2.5 accumulated near Sanmenxia with the influence of downslope, zonal wind, and topography. The main regional transport paths could be summarized into an eastern path, a northern path, and a western path during the severe haze episodes. The PM2.5 source apportionment revealed by an on-line tracer-tagged of the Nested Air Quality Prediction Model System (NAQPMS) showed that the main regional sources of Sanmenxia were Yuncheng, Sanmenxia, and Weinan. The contribution to PM2.5 concentration in Sanmenxia was 39%, 25%, and 11%, respectively. The northern path had the most important impact on Sanmenxia. The results can provide scientific basis for the establishment of severe haze control in Sanmenxia and regional joint control. [ABSTRACT FROM AUTHOR]

Air pollution is becoming increasingly serious along with social and economic development in the southwest of China. The distribution characteristics of particle matter (PM) were studied in Chengdu from 2016 to 2017, and the changes of PM bearing water-soluble ions and heavy metals and the distribution of secondary ions were analyzed during the haze episode. The results showed that at different pollution levels, heavy metals were more likely to be enriched in fine particles and may be used as a tracer of primary pollution sources. The water-soluble ions in PM2.5 were mainly Sulfate-Nitrate-Ammonium (SNA) accounting for 43.02%, 24.23%, 23.50%, respectively. SO42−, NO3−, NH4+ in PM10 accounted for 34.56%, 27.43%, 19.18%, respectively. It was mainly SO42− in PM at Clean levels (PM2.5 = 0~75 μg/m3, PM10 = 0~150 μg/m3), and mainly NH4+ and NO3− at Light-Medium levels (PM2.5 = 75~150 μg/m3, PM10 = 150~350 μg/m3). At Heavy levels (PM2.5 = 150~250 μg/m3, PM10 = 350~420 μg/m3), it is mainly SO42− in PM2.5, and mainly NH4+ and NO3− in PM10. The contribution of mobile sources to the formation of haze in the study area was significant. SNA had significant contributions to the PM during the haze episode, and more attention should be paid to them in order to improve air quality. [ABSTRACT FROM AUTHOR]

We report the results of year-long PM2.5 (particulate matter less than 2.5 µm in diameter) simulations over Northeast Asia for the base year of 2013 under the framework of the Long-range Transboundary Air Pollutants in Northeast Asia (LTP) project. LTP is a tripartite project launched by China, Japan, and Korea for cooperative monitoring and modeling of the long-range transport (LRT) of air pollutants. In the modeling aspect in the LTP project, each country's modeling group employs its own original air quality model and options. The three regional air quality models employed by the modeling groups are WRF-CAMx, NHM-RAQM2, and WRF-CMAQ. PM2.5 concentrations were simulated in remote exit-and-entrance areas associated with the LRT process over China, Japan, and Korea. The results showed apparent bias that remains unexplored due to a series of uncertainties from emission estimates and inherent model limitations. The simulated PM10 levels at seven remote exit-and-entrance sites were underestimated with the normalized mean bias of 0.4 ± 0.2. Among the four chemical components of PM2.5 (SO42−, NO3−, organic carbon (OC), and elemental carbon (EC)), the largest inter-model variability was in OC, with the second largest discrepancy in NO3−. Our simulation results also indicated that under considerable SO42− levels, favorable environments for ammonium nitrate formation were found in exit-and-entrance areas between China and Korea, and gas-aerosol partitioning for semi-volatile species of ammonium nitrate could be fully achieved prior to arrival at the entrance areas. Other chemical characteristics, including NO3−/SO42− and OC/EC ratios, are discussed to diagnose the LRT characteristics of PM2.5 in exit-and-entrance areas associated with transboundary transport over China, Japan, and Korea. [ABSTRACT FROM AUTHOR]

Road dust is one of the primary sources of particulate matter which has implications for air quality, climate and health. With the aim of characterizing the emissions, in this study, a bottom-up approach of county level emission inventory from paved road dust based on field investigation was developed. An inventory of high-resolution paved road dust (PRD) emissions by monthly and spatial allocation at 1 km × 1 km resolution in Harbin in 2016 was compiled using accessible county level, seasonal data and local parameters based on field investigation to increase temporal-spatial resolution. The results demonstrated the total PRD emissions of TSP, PM10, and PM2.5 in Harbin were 270,207 t, 54,597 t, 14,059 t, respectively. The temporal variation trends of pollutant emissions from PRD was consistent with the characteristics of precipitation, with lower emissions in winter and summer, and higher emissions in spring and autumn. The spatial allocation of emissions has a strong association with Harbin's road network, mainly concentrating in the central urban area compared to the surrounding counties. Through scenario analysis, positive control measures were essential and effective for PRD pollution. The inventory developed in this study reflected the level of fugitive dust on paved road in Harbin, and it could reduce particulate matter pollution with the development of mitigation strategies and could comply with air quality modelling requirements, especially in the frigid region of northeastern China. [ABSTRACT FROM AUTHOR]

Ambient exposure to particulate matter (PM) air pollution is known to have an adverse effect on public health worldwide. Rapid increase rates of economic and urbanization, industrial development, and environmental change in China have exacerbated the occurrence of air pollution. This study examines the temporal and spatial distribution of PM on national, regional and local scales in China during 2014–2016. The relationships between the PM2.5 concentration rising rate (PMRR) and meteorological parameters (wind speed and wind direction) are discussed. The dataset of Air Quality Index (AQI), PM10 (PM diameter < 10 μ m ) and PM2.5 (PM diameter < 2.5 μ m ) were collected in 169, 369, and 367 cities in 2014, 2015, and 2016 over China, respectively. The results show that the air quality has been generally improved on the national scale, but deteriorated locally in areas such as the Feiwei Plain. The northwest China (NW) and Beijing-Tianjin-Hebei (BTH) regions are the worst areas of PM pollution, which are mainly manifested by the excessive PM10 caused by blowing dust in spring in NW and the intensive emissions of PM2.5 in winter in BTH. With the classified seven geographic regions, we demonstrate the significant spatial difference and seasonal variation of PM concentration and PM2.5/PM10 ratio, which indicate different emission sources. Furthermore, the dynamic analysis of the PM2.5 pollution process in 11 large urban cities shows dramatic effects of wind speed and wind direction on the PM2.5 loadings. [ABSTRACT FROM AUTHOR]

Ground-level ozone (O3), which is mainly from the photochemical reactions of NOx and volatile organic compounds (VOCs), has become a crucial pollutant obstructing air quality improvement in China. Understanding the composition, temporal variability and source apportionment of VOCs is necessary for determining effective control measures to minimize VOCs and their related photochemical pollution. To provide a comprehensive analysis of VOC sources and their contributions to ozone formation in the city of Chengdu—a megacity with the highest rates of industrial and economic development in southwest China—we conducted a one-month monitoring project at three urban sites (Shuangliu, Xindu, Junpingjie; SL, XD and JPJ, respectively) along the main north–south meteorological transport channel before and during the implemented control measures. Alkanes were the dominant group at each site, contributing to around 50% of the observed total VOCs, followed by oxygen-containing VOCs (OVOCs), aromatics, halohydrocarbons and alkenes. During the control period, the mixing ratios of most measured VOC species decreased, and O3 concentrations were down by at least 20%. VOC species experiencing the most effect from control were aromatics and OVOCs, which had higher O3 formation reactivity. This indicated that the control policies had significant influence on reductions of reactive VOC species. We also identified VOC sources at SL and XD using positive matrix factorization (PMF) and assessed their contributions to photochemical O3 formation by calculating the O3 formation potential (OFP) based on mass concentrations and maximum incremental reactivity of related VOCs. Five dominant VOC sources were identified, with the highest contributions from vehicular exhaust and fuel evaporation before control, followed by solvent utilization, biogenic background and secondary formation, and industrial emissions. Contribution from vehicular exhaust was reduced the most at SL, while at XD, secondary formation VOCs decreased significantly. VOCs from vehicular and industrial emissions and solvent utilization were found to be the dominant precursors for OFPs, particularly the species of xylenes, toluene and propene. Our results therefore suggest that priority should be given to the alleviation of photochemical pollutants for effective control of O3 formation in Chengdu. The findings from this work have important implications for formulating effective emission control policies in Chengdu. [ABSTRACT FROM AUTHOR]

Agricultural emissions are crucial to regional air quality in the autumn and spring due to the intense agricultural activities in Northeast China. However, information on rural ambient particulate matter (PM) in Northeast China is rare, limiting the accurate estimation of agricultural atmospheric particulate matter emissions. In this study, we monitored hourly ambient PM2.5 (PM with a diameter of less than 2.5 μm) concentrations and analyzed daily chemical components (i.e., water-soluble ions, trace elements, organic carbon, and element carbon) at a rural site in Northeast China during the autumn and spring and assessed the impact of agricultural activities on atmospheric PM2.5 concentrations. The results showed that the daily average concentrations of PM2.5 were 143 ± 109 (range: 39–539) μg m−3 from 19 October to 23 November 2017 (i.e., typical harvesting month) and 241 ± 189 (range: 97–976) μg m−3 from 1 April to 13 May 2018 (i.e., typical tilling month). In autumn, the ambient PM2.5 concentrations were high with a Southwest wind, while a Southeast wind caused high PM2.5 concentrations during spring in the rural site. The concentrations of selected water-soluble ions, trace elements, and carbonaceous fractions accounted for 33%, 4%, and 26% of PM2.5 mass concentrations, respectively, in autumn and for 10%, 5%, and 3% of PM2.5 mass concentrations, respectively, in spring. On the basis of the component analysis, straw burning, agricultural machinery, and soil dust driven by wind and tilling were the main contributors to high rural PM2.5 concentrations. In addition, the increasing coal combustion around the rural site was another important source of PM2.5. [ABSTRACT FROM AUTHOR]

Fine particulate matter (PM2.5) poses a risk to human health. In January 2017, the PM2.5 pollution in China was severe, and the average PM2.5 concentration had increased by 14.7% compared to that in January 2016. Meteorological conditions greatly influence PM2.5 pollution. The relationship between PM2.5 and meteorological factors was assessed using monitoring data and the Community Multiscale Air Quality modeling system (CMAQ) was used to quantitatively evaluate the impacts of variations of meteorological conditions on PM2.5 pollution. The results indicate that variations of meteorological conditions between January 2017 and January 2016 caused an increase of 13.6% in the national mean concentration of PM2.5. Unlike the Yangtze River Delta (YRD), where meteorological conditions were favorable, unfavorable meteorological conditions (such as low wind speed, high humidity, low boundary layer height and low rainfall) contributed to PM2.5 concentration worsening by 29.7%, 42.6% and 7.9% in the Beijing-Tianjin-Hebei (JJJ) region, the Pearl River Delta (PRD) region and the Chengdu-Chongqing (CYB) region, respectively. Given the significant influence of local meteorology on PM2.5 concentration, more emphasis should be placed on employing meteorological means to improve local air quality. [ABSTRACT FROM AUTHOR]