Contribution of meteorological factors to particulate pollution during winters in Beijing

Comparison of PM10 emission flux of two fugitive area sources based on the real-time flux monitoring results

Due to a high concentration of particulate matter (PM10), the Korean Peninsula experienced its first poor air quality event of the year between November 19 and November 26, 2021. This study analyzes the reasons behind the occurrence of high-concentration PM10, using the real-time PM10 fugitive emission fluxes and meteorological data measured at two landfills for fly ash of coal-fired power plants located on the west coast. The real-time fugitive emission fluxes of PM10 were estimated at two different locations by a flux-gradient technique based on the eddy covariance method. The measurement results show a weak correlation between PM10 and various meteorological factors in the two places when PM10 levels are low. However, high PM10 concentrations were found to be strongly associated with the relative humidity of site A and the friction velocity of site B, respectively. High emission fluxes were observed at both sites under elevated temperature, high humidity, low wind speed, low frictional velocity, and atmospheric instability. The variation in weather patterns witnessed during periods of high PM10 concentrations in the two locations indicates that the causes of PM10 accumulation are different. The study demonstrates that the gradient-flux method's real-time measurement of fugitive emissions can explain the origin of high PM10 levels and provide essential data to efficiently regulate PM10.

Large contributions of emission reductions and meteorological conditions to the abatement of PM2.5 in Beijing during the 24th Winter Olympic Games in 2022

To guarantee the blue skies for the 2022 Winter Olympics held in Beijing and Zhangjiakou from February 4 to 20, Beijing and its surrounding areas adopted a series of emission control measures. This provides an opportunity to determine the impacts of large-scale temporary control measures on the air quality in Beijing during this special period. Here, we applied the WRF-CMAQ model to quantify the contributions of emission reduction measures and meteorological conditions. Results show that meteorological conditions in 2022 decreased PM2.5 in Beijing by 6.9 and 11.8 µg/m3 relative to 2021 under the scenarios with and without emission reductions, respectively. Strict emission reduction measures implemented in Beijing and seven neighboring provinces resulted in an average decrease of 13.0 µg/m3 (-41.2%) in PM2.5 in Beijing. Over the entire period, local emission reductions contributed more to good air quality in Beijing than nonlocal emission reductions. Under the emission reduction scenario, local, controlled regions, other regions, and boundary conditions contributed 47.7%, 42.0%, 5.3%, and 5.0% to the PM2.5 concentrations in Beijing, respectively. The results indicate that during the cleaning period with the air masses from the northwest, the abatements of PM2.5 were mainly caused by local emission reductions. However, during the potential pollution period with the air masses from the east-northeast and west-southwest, the abatements of PM2.5 were caused by both local and nonlocal emission reductions almost equally. This implies that regional coordinated prevention and control strategies need to be arranged scientifically and rationally when heavy pollution events are forecasted.

Ultra-high-resolution mapping of ambient fine particulate matter to estimate human exposure in Beijing

With the decreasing regional-transported levels, the health risk assessment derived from fine particulate matter (PM2.5) has become insufficient to reflect the contribution of local source heterogeneity to the exposure differences. Here, we combined the both ultra-high-resolution PM2.5 concentration with population distribution to provide the personal daily PM2.5 internal dose considering the indoor/outdoor exposure difference. A 30-m PM2.5 assimilating method was developed fusing multiple auxiliary predictors, achieving higher accuracy (R2 = 0.78-0.82) than the chemical transport model outputs without any post-simulation data-oriented enhancement (R2 = 0.31-0.64). Weekly difference was identified from hourly mobile signaling data in 30-m resolution population distribution. The population-weighted ambient PM2.5 concentrations range among districts but fail to reflect exposure differences. Derived from the indoor/outdoor ratio, the average indoor PM2.5 concentration was 26.5 μg/m3. The internal dose based on the assimilated indoor/outdoor PM2.5 concentration shows high exposure diversity among sub-groups, and the attributed mortality increased by 24.0% than the coarser unassimilated model.

Quantifying the scale-dependent relationships of PM2.5 and O3 on meteorological factors and their influencing factors in the Beijing-Tianjin-Hebei region and surrounding areas

To investigate the variations of PM2.5 and O3 and their synergistic effects with influencing factors at different time scales, we employed Bayesian estimator of abrupt seasonal and trend change to analyze the nonlinear variation process of PM2.5 and O3. Wavelet coherence and multiple wavelet coherence were utilized to quantify the coupling oscillation relationships of PM2.5 and O3 on single/multiple meteorological factors in the time-frequency domain. Furthermore, we combined this analysis with the partial wavelet coherence to quantitatively evaluate the influence of atmospheric teleconnection factors on the response relationships. The results obtained from this comprehensive analysis are as follows: (1) The seasonal component of PM2.5 exhibited a change point, which was most likely to occur in January 2017. The trend component showed a discontinuous decline and had a change point, which was most likely to appear in February 2017. The seasonal component of O3 did not exhibit a change point, while the trend component showed a discontinuous rise with two change points, which were most likely to occur in July 2018 and May 2017. (2) The phase and coherence relationships of PM2.5 and O3 on meteorological factors varied across different time scales. Stable phase relationships were observed on both small- and large-time scales, whereas no stable phase relationship was formed on medium scales. On all-time scales, sunshine duration was the best single variable for explaining PM2.5 variations and precipitation was the best single variable explaining O3 variations. When compared to single meteorological factors, the combination of multiple meteorological factors significantly improved the ability to explain variations in PM2.5 and O3 on small-time scales. (3) Atmospheric teleconnection factors were important driving factors affecting the response relationships of PM2.5 and O3 on meteorological factors and they had greater impact on the relationship at medium-time scales compared to small- and large-time scales.

Spatiotemporal characteristics and socioeconomic factors of PM2.5 heterogeneity in mainland China during the COVID-19 epidemic

Spatiotemporal variation of PM_2.5 in 2018 and 2020 were compared to analyze the impacts of COVID-19, the spatial heterogeneity of PM_2.5, and meteorological and socioeconomic impacts of PM_2.5 concentrations heterogeneity in China in 2020 were investigated. The results showed that the annual average PM_2.5 concentration in 2020 was 32.73 μg/m³ existing a U-shaped variation pattern, which has decreased by 6.38 μg/m³ compared to 2018. A consistent temporal pattern was found in 2018 and 2020 with significant high values in winter and low in summer. PM_2.5 declined dramatically in eastern and central China, where are densely populated and economically developed areas during the COVID-19 epidemic compared with previous years, indicating that the significantly decline of social activities had an important effect on the reduction of PM_2.5 concentrations. The lowest PM_2.5 was found in August because that precipitation had a certain dilution effect on pollutants. January was the most polluted due to centralized coal burning for heating in North China. Overall, the PM_2.5 concentrations in China were spatially agglomerated. The highly polluted contiguous zones were mainly located in northwest China and the central plains city group, while the coastal area and Inner Mongolia were areas with good air quality. Negative correlations were found between natural factors (temperature, precipitation, wind speed and relative humidity) and PM_2.5 concentrations, with precipitation has the greatest impact on PM_2.5, which are beneficial for reducing PM_2.5 concentrations. Among the socio-economic factors, proportion of the secondary industry, number of taxis, per capita GDP, population, and industrial nitrogen oxide emissions have positive correlation effects on PM_2.5, while the overall social electricity consumption, industrial sulfur dioxide emissions, green coverage in built-up areas, and total gas and liquefied gas supply have negative correlation effects on the PM_2.5.

Estimating particulate matter concentrations and meteorological contributions in China during 2000-2020

Estimating the effects of airborne particulate matter (PM) on climate and human health is highly dependent on the accurate prediction of its concentration and size distribution. High-complexity machine learning models have been widely used for PM concentration prediction, but such models are often considered as "black boxes", lacking interpretability. Here, a simple structure lightGBM model is built for ground PM estimation, and the SHAP approach is used to separate the meteorological contributions due to its strong influence on PM concentration. The models show good performance with correlation coefficient (R²) of 0.84-0.88, 0.80-0.85, and 0.71-0.79, for PM_2.5, PM₁₀, and PM_2.5-10 (2.5-10 μm), respectively. The lightGBM model trains 45 times faster than the XGBoost model while showing similar accuracy. More importantly, the models have small performance gaps between training and predicting (delta R²: 0.07-0.12), effectively reducing overfitting risk. The PM datasets (10 km daily) of three size ranges are then generated over China from 2000 to 2020. The SHAP method shows good agreement with the meteorological normalization approach in separating the meteorological contributions (R² > 0.5). In the Beijing-Tianjin-Hebei region (BTH), meteorology has greater influence on PM_2.5-10 (-5.66%-9.99%) than PM_2.5 and PM₁₀. In the Yangtze River Delta (YRD), and the Pearl River Delta (PRD), albedo has a large contribution to PM_2.5 concentration under the influence of solar radiation. Notably, relative humidity (RH) has different seasonal effects on PM of three size ranges. In the BTH region, RH has negative effects on PM_2.5 (-0.52 μg/m³) and positive effects on PM₁₀ (1.01 μg/m³) and PM_2.5-10 (3.39 μg/m³) in spring, but has opposite effects in summer. The results of SHAP approach are consistent with existing conclusions and imply its feasibility in explaining haze formation. The generated PM datasets are useful in health assessment, environmental management, and climate change studies.

Spatio-temporally differentiated impacts of temperature inversion on surface PM2.5 in eastern China

Temperature inversion (TI) is one of the meteorological conditions that significantly affect regional air quality. Knowledge gap regarding the impacts of TI on surface PM_2.5 in different topographies still existed. In the present study, the occurrence frequency, temperature lapse rate (TLR), depth, and the diurnal variations of TI, surface-based TI (SBTI), elevated TI (ElTI), and multiple layers of TIs (MultiTI) and their impacts on near-surface PM_2.5 concentrations over eastern China that covers a range of topographies and climates, are systematically investigated based on global reanalysis ERA5 and the nationwide monitoring PM_2.5 dataset from 2014 to 2020. TIs occurred mostly in the early morning. Different types of TIs present distinctive seasonal and spatial patterns. The majority of SBTIs and ElTIs occurred during nighttime in northern China and daytime in southern China, respectively, as the result of their formation mechanisms. SBTIs usually had larger TLR while ElTIs had deeper depth. SBTIs showed strong enhancement effects on PM_2.5 concentration over the study domain while ElTIs showed more obvious impacts on northern nocturnal PM_2.5. The peak time of PM_2.5 was found around 18:00-22:00 LST, and TLR and depth of TIs are thought to be more relevant to PM_2.5 peak concentration due to their coincident peak times. The strength of TIs is therefore more crucial in regulating PM_2.5 than its occurrence frequency. Based on statistical analysis, our study provided a large picture of the generic spatiotemporal patterns of TIs and illustrated the impacts of different TIs on surface PM_2.5 pollution on a diurnal basis. For a deeper understanding of the formation of PM_2.5 pollution, more attention needs to be paid to the nocturnal PM_2.5 not only at surface level but also at higher levels in the presence of TIs.

Drivers of PM2.5 in the urban agglomeration on the northern slope of the Tianshan Mountains, China

Fine particulate matter (PM_2.5) is a major source of air pollution in China. Although there have been many studies of the drivers of PM_2.5 pollution in the megacities clustered in eastern China, the behavior of PM_2.5 in the northwestern urban agglomeration is not well understood. This study used near-surface observation data for 2015-2019 obtained from the national air environmental monitoring network to examine variation in PM_2.5 in the urban agglomeration on the northern slopes of the Tianshan Mountains (UANSTM). Two-factor interaction provided new insights into the dominant factors of PM_2.5 in the study region. The annual average PM_2.5 concentrations over the study period was 54.3 μg/m³, with an exceedance rate of 23.3%. Wavelet analysis showed two dominant cycles of 320-370 d and 150-200 d with high pollution events occurring in winter. The generalized additive model (GAM) contained linear functions of pressure, non-linear functions of SO₂, NO₂, relative humidity, sunshine duration and temperature. The two most primary variables, NO₂ and SO₂, represent 20.65% and 19.54% of the total deviance explained, respectively, while the meteorological factors account for 36.1% of the total deviance explained. In addition, the interaction between NO₂ and other factors had the strongest effect on PM_2.5. The deviance explained in the two factor interaction model (88.5%) was higher than that in the single factor model (78.4%). Our study emphasized that interaction between meteorological factors and pollutant emissions enhanced the impact on PM_2.5 compared with individual factors, which can provide a scientific basis for developing effective emission reduction strategies in UANSTM.

Roles of Ambient Temperature and PM2.5 on Childhood Acute Bronchitis and Bronchiolitis from Viral Infection

Studies have associated the human respiratory syncytial virus which causes seasonal childhood acute bronchitis and bronchiolitis (CABs) with climate change and air pollution. We investigated this association using the insurance claims data of 3,965,560 children aged ≤ 12 years from Taiwan from 2006−2016. The monthly average incident CABs increased with increasing PM2.5 levels and exhibited an inverse association with temperature. The incidence was 1.6-fold greater in January than in July (13.7/100 versus 8.81/100), declined during winter breaks (February) and summer breaks (June−August). The highest incidence was 698 cases/day at <20 °C with PM2.5 > 37.0 μg/m3, with an adjusted relative risk (aRR) of 1.01 (95% confidence interval [CI] = 0.97−1.04) compared to 568 cases/day at <20 °C with PM2.5 < 15.0 μg/m3 (reference). The incidence at ≥30 °C decreased to 536 cases/day (aRR = 0.95, 95% CI = 0.85−1.06) with PM2.5 > 37.0 μg/m3 and decreased further to 392 cases/day (aRR = 0.61, 95% CI = 0.58−0.65) when PM2.5 was <15.0 μg/m3. In conclusion, CABs infections in children were associated with lowered ambient temperatures and elevated PM2.5 concentrations, and the high PM2.5 levels coincided with low temperature levels. The role of temperature should be considered in the studies of association between PM2.5 and CABs.

Evaluating the impacts of burning biomass on PM2.5 regional transport under various emission conditions

The fine particulate matter (PM_2.5) emitted by burning biomass has become the main source of pollution in cities; this pollution seriously threatens the ecosystem and inhabitants' health. A major challenge in dealing with this issue is the uncertainty regarding the influence of burning biomass on PM_2.5 regional transport. In this study, Harbin-Changchun Megalopolis is the research area. Using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model combined with satellite data and PM_2.5 monitoring data, we quantitatively analyzed the regional transport of PM_2.5 from burning biomass upwind of each city under different emission conditions. Conditions at burn sites, especially emission intensity and meteorological factors, as well as transport distance play significant roles in the regional transport of PM_2.5. Higher emission intensity aggravated the concentration of downwind PM_2.5, at most 19.7 μg ⋅ m^-3. Shorter transport distance strengthened the impact of biomass burning on downstream PM_2.5 by weakening elimination, which could be up to 96.8 μg ⋅ m^-3. Moreover, meteorological factors at fire points were closely related to the transport of PM_2.5. First, lower planetary boundary layer height could enhance the transport of PM_2.5 from the burning biomass by inhibiting vertical diffusion, and the enhancement could be up to 46.1 μg ⋅ m^-3. Second, compared to strong wind, light wind caused the weaker dilution, enhancing PM_2.5 regional transport by as much as 32.5 μg ⋅ m^-3. Third, relatively humidity at 30%-40% had the strongest effect in facilitating the transport of PM_2.5 from burning biomass. We conclude that comprehensively considering these three factors, namely the emission intensity, transport distance and meteorological factors at burn sites can facilitate the cross-regional development of accurate prediction models and effective pollution control measures.

Spatial Disparity of Meteorological Impacts on Carbon Monoxide Pollution in China during the COVID-19 Lockdown Period

Lockdown due to the novel coronavirus disease 2019 (COVID-19) pandemic offers a unique opportunity to study the factors governing the variation in air pollution. A number of studies have investigated the cause underlying the occurrence of heavy haze pollution around the world during the lockdown period. However, information about spatiotemporal variations in gaseous pollutants and detailed quantifications of potential meteorological (METRO) impacts are limited. Ground measurements show that carbon monoxide (CO) pollution deteriorated in northern China despite strict control of human and industrial activities during the lockdown period in early 2020. In this study, a four-dimensional decomposition model was used to quantitatively extract the METRO impacts on the CO pollution over China. The results show that weakened winds elevated CO concentrations near Beijing and in northeastern China. Increased temperatures slightly elevated CO concentrations in northern and eastern China but reduced CO concentrations in northwestern China. Remarkable amounts of CO increases in northern China (e.g., by 0.21 mg/m3 within Beijing) were explained by anomalously high humidity, which could be associated with an enhanced interaction between aerosol and the boundary layer. After excluding the METRO impacts, the CO concentrations drastically declined across China (e.g., by 0.22 mg/m3 within Beijing), indicating that the lockdown indeed greatly lessened CO concentrations. However, the adverse METRO conditions counteracted the beneficial outcomes of emission reductions, leading to a deterioration of the CO pollution in northern China. These results indicate that the METRO factors can play a critical role in worsening air pollution despite a strict control of anthropogenic emissions.

Local synergies and antagonisms between meteorological factors and air pollution: A 15-year comprehensive study in the Sydney region

Associated with rapid urbanization and escalation of bushfire events, Sydney has experienced significant air quality degradation in the XXI century. In this study, we present a 15-year retrospective analysis on the influence of individual meteorological factors on major air pollutants (NO₂, O₃, PM₁₀ and PM_2.5) at 14 different sites in Greater Sydney and Illawarra. By applying a newly developed "zooming in" approach to long-term ground-based data, we disclose general, seasonal, daily and hourly patterns while increasing the level of spatial associativity. We provide evidence on the pivotal role played by urbanization, sprawling dynamics, global warming and bushfires on local meteorology and air pollution. We strike associations between temperature and O₃, both as average trends and extremes, on account of increasing heat island effects. The role of wind in a coastal-basin environment, influenced by a vast desert biome inland, is investigated. A steady trend towards stagnation is outlined, boosted by enhanced urban roughness and intensified heat island circulation. Relative humidity is also crucial in the modulation between NO₂ and O₃. With a sharp tendency towards drier and hotter microclimates, NO₂ levels dropped by approximately 50% over the years at all locations, while O₃'s median levels almost doubled in the last 10 years. Further, O₃ and PMs shifted towards more frequent extreme events, strongly associated with the exacerbation of bushfire events. Such results suggest an urgent need to prioritize emission control, building air tightness improvement and urban heat mitigation, towards a future-proof governance in Sydney and similar regions in the world.

Seasonal discrepancies in peroxyacetyl nitrate (PAN) and its correlation with ozone and PM2.5: Effects of regional transport from circumjacent industrial cities

Peroxyacetyl nitrate (PAN) is the most important reservoir of nitrogen oxides, with effects on atmospheric oxidation capacity and regional nitrogen distribution. The first yearlong observational study of PAN was conducted from September 2018 to August 2019 at a suburban site and an urban site in Zhengzhou, Henan Province, central China. Compared with studies over the past two decades, summer PAN pollution at the suburban site and winter PAN pollution at both sites were more significant, with annual average concentrations of 1.96 ± 1.44 and 2.01 ± 1.59 ppbv, respectively. Seasonal PAN discrepancies between the urban and suburban areas were analyzed in detail. Active PAN formation, regional transport, photochemical precursors, and PAN lifetime played key roles during seasons with elevated PAN (winter and spring). According to the results of cluster analysis and potential source contribution function analysis, during the cold months, short-distance air mass transport from the east, south, and southeast of Henan Province and southern Hebei Province increased PAN pollution in urban Zhengzhou. PAN source areas were located in circumjacent industrial cities surrounding Zhengzhou except in the northeastern direction. Based on the relationships between pollutant concentrations, wind speed, and wind direction, a strong positive correlation between PAN and PM_2.5 (and O₃) existed in winter due to their joint transport. A slow-moving, low-height air mass passed through surrounding industrial cities before reaching the study area, carrying both pollutants and leading to strong consistency between PAN and O₃ levels. The long-term PAN characteristics described in this study will help clarify the causes of regional air pollution in inland city agglomerations. Moreover, the PAN correlations and joint transport of PAN and PM_2.5 (or O₃) support the use of PAN as an indicator of air pollution introduced from surrounding industrial areas.

Analyzing Characteristics of Particulate Matter Pollution in Open-Pit Coal Mines: Implications for Green Mining

The particulate pollution in the open-pit coal mines of China is particularly severe in winter. The aim of this study is to understand the pollution characteristics of particulate matter (PM) in winter and provide a basis for the prevention and control of particulate pollution. We took the problem of PM concentration at the bottom of the Haerwusu Open-pit Coal Mine (HOCM) as the research object. Dust monitoring equipment at two measurement points at different heights were positioned for continuous monitoring of the PM concentration. The data for three months were gathered. Statistical analyses were performed to analyze the variation characteristics of the PM and its relationship with meteorological factors. The results show that the average PM concentration in the study area is below the average daily limit of the China National Ambient Air Quality Standard (GB 3095-2012). However, the average concentration of PM10 exceeded the national limit in December. The order of PM concentration is observed as December > January > February. The correlation of PM is found to be positive with humidity and negative with wind speed. Temperature is found to be positively correlated with PM in December, while it is negative in January. At the same time, the temperature difference in December is negatively correlated with PM concentration. Under the combined action of multiple meteorological factors, the magnitude of the impact on the PM concentration at the bottom of the pit in winter is humidity > temperature > wind speed > temperature difference (inverse temperature intensity). In conclusion, PM2.5 is found to be more sensitive to environmental factors. The results of this study are particularly useful to progress in green mining.

An improved decomposition method to differentiate meteorological and anthropogenic effects on air pollution: A national study in China during the COVID-19 lockdown period

Although the effects of meteorological factors on severe air pollution have been extensively investigated, quantitative decomposition of the contributions of meteorology and anthropogenic factors remains a big challenge. The novel coronavirus disease 2019 (COVID-19) pandemic affords a unique opportunity to test decomposition method. Based on a wind decomposition method, this study outlined an improved method to differentiate complex meteorological and anthropogenic effects. The improved method was then applied to investigate the cause of unanticipated haze pollution in China during the COVID-19 lockdown period. Results from the wind decomposition method show that weakened winds increased PM_2.5 concentrations in the Beijing-Tianjin area and northeastern China (e.g., by 3.19 μg/m³ in Beijing). Using the improved decomposition method, we found that the combined meteorological effect (e.g., drastically elevated humidity levels and weakened airflow) substantially increased PM_2.5 concentrations in northern China: the most substantial increases were in the Beijing-Tianjin-Hebei region (e.g., by 26.79 μg/m³ in Beijing). On excluding the meteorological effects, PM_2.5 concentrations substantially decreased across China (e.g., by 21.84 μg/m³ in Beijing), evidencing that the strict restrictions on human activities indeed decreased PM_2.5 concentrations. The unfavorable meteorological conditions, however, overwhelmed the beneficial effects of emission reduction, causing the severe haze pollution. These results indicate that the integrated meteorological effects should be considered to differentiate the meteorological and anthropogenic effects on severe air pollution.

Key Points in Air Pollution Meteorology

Although emissions have a direct impact on air pollution, meteorological processes may influence inmission concentration, with the only way to control air pollution being through the rates emitted. This paper presents the close relationship between air pollution and meteorology following the scales of atmospheric motion. In macroscale, this review focuses on the synoptic pattern, since certain weather types are related to pollution episodes, with the determination of these weather types being the key point of these studies. The contrasting contribution of cold fronts is also presented, whilst mathematical models are seen to increase the analysis possibilities of pollution transport. In mesoscale, land-sea and mountain-valley breezes may reinforce certain pollution episodes, and recirculation processes are sometimes favoured by orographic features. The urban heat island is also considered, since the formation of mesovortices determines the entry of pollutants into the city. At the microscale, the influence of the boundary layer height and its evolution are evaluated; in particular, the contribution of the low-level jet to pollutant transport and dispersion. Local meteorological variables have a major influence on calculations with the Gaussian plume model, whilst some eddies are features exclusive to urban environments. Finally, the impact of air pollution on meteorology is briefly commented on.

Development of a noise-assisted multivariate empirical mode decomposition framework for characterizing PM 2.5 air pollution in Taiwan and its relation to hydro-meteorological factors

To better understand air pollution problems, the relationships between PM_2.5 and hydro-meteorological variables are studied using a state-of-the-art multivariate nonlinear and non-stationary filtering method, noise-assisted multivariate empirical mode decomposition (NAMEMD), and the time-dependent intrinsic correlation (TDIC) algorithm. Three characteristic scales (annual, diurnal and semi-diurnal) are shown to be significant to PM2.5 characterization, based on using NAMEMD filtering. Temporal fluctuations of local correlations among PM_2.5 and hydro-meteorological variables are presented. On diurnal and semi-diurnal scales, seasonal variation of the local correlation between temperature and humidity is observed. A combined wind speed and direction analysis can be conducted using the NAMEMD-based algorithm. The pollutant roses that are generated from the reconstructed wind directions reveal the sources of PM_2.5 on different scales. PM_2.5 is found to be related to land breeze at the diurnal scale and to winter monsoons at the annual scale. The scale-dependent wind direction that contributes to the increase of PM_2.5 can be identified.

Source-specific ecological risk analysis and critical source identification of heavy metals in road dust in Beijing, China

To explore the spatial variation of source-specific ecological risks and identify critical sources of heavy metals in road dust, 36 road dust samples collected in Beijing in March 2017 were analyzed for heavy metals. A new method that takes into consideration the heavy-metal toxic response and is flexible to changes in the number of calculated heavy metals, called the Nemerow integrated risk index (NIRI), was developed for ecological risk assessment. The NIRI indicated that heavy metals posed considerable to high risks at the majority of sites, and 22 % of the sites suffered extreme risk in spring (NIRI > 320). Four main sources were identified based on positive matrix factorization (PMF): traffic exhaust, fuel combustion, construction, and use of pesticides and fertilizers. Owing to the lower toxic response factors of representative heavy metals of fuel combustion than those of other sources, although fuel combustion had the highest contribution (34.21 %) to heavy metals in spring, it only contributed 5.57 % to ecological risks. Critical sources and critical source areas were determined by considering the contributions to both heavy metals and ecological risks. The use of pesticide and fertilizer and traffic-related exhaust were identified as critical sources of heavy metals in spring. Source-specific ecological risks and critical sources of heavy metals changed with the changing seasons, which suggests that different strategies should be adopted in different seasons.

Estimating the remaining atmospheric environmental capacity using a single-box model in a high pollution risk suburb of Chengdu, China

The atmospheric pollution has been the public attention in recent years. In order to better coordinate economic development and atmospheric environmental management, China introduced the concept of atmospheric environmental capacity (AEC). The remaining atmospheric environmental capacity (RAEC) calculated by existing atmospheric pollution sources and AEC is an important basis for regional development and environmental protection. The RAEC of the high-pollution risk suburb of Chengdu in 2015 was estimated by the single-box model and analyzed on multiple time scales. The results show that the RAEC of SO₂ and NO₂ in this region is 3299 t/a and 2849 t/a, respectively under the annual time scale. However, in the daily time scale, the RAEC of NO₂ is negative for 3 days, that is, there are 3 days with serious air pollution. Therefore, it is not appropriate to plan the industrial area only by relying on annual RAEC. Especially, RAEC displays inter-seasonal and monthly variability. On the one hand, in plain areas with low wind speed and little change in wind direction, achieving the prediction of atmospheric mixing layer height could give early warning of atmospheric pollution events. On the other hand, different management measures are taken on different time scales. On a long timescale, the regional energy structure should be optimized. On seasonal and monthly time scales, the production plans should be adapted to RAEC. On the daily time scale, it mainly deals with the serious atmospheric pollution accident timely.

Characteristics of Air Pollution and Their Relationship with Meteorological Parameters: Northern Versus Southern Cities of China

Air pollution shows a generally decreasing trend from the north to the south in China since 2013 (GB3095-2012, the current standard for monitoring). However, an opposite observation was recorded in 2017, especially in winter. In this study, we collected monitoring data of six air pollutants in 2016 and 2017, from a northern city (Beijing) and a southern city (Nanjing) for comparison. As air pollution was highly dependent upon meteorological conditions, we further analyzed their relationships to explain this abnormal phenomenon. Seasonal averaged PM2.5, PM10, SO2, CO, and NO2 were negatively correlated with wind scale (WS) while 8-h O3 exhibited an opposite relationship. Relative humidity (RH) has opposite effects on the concentrations of different pollutants in Beijing and Nanjing. The 8-h O3 showed the closest positive correlation with temperature (T), which is due to its formation mechanism. In Beijing, decreased RH, together with more wind from northwest in winter, resulted in an improved air quality in 2017. In Nanjing, WS, RH, T, and wind direction fluctuated within a narrow range in each season, leading to relatively stable pollutant concentrations. These results suggest that meteorological conditions are important factors to evaluate the air quality and implement control measures.

Spatiotemporal Variability and Influencing Factors of Aerosol Optical Depth over the Pan Yangtze River Delta during the 2014-2017 Period

Large amounts of aerosol particles suspended in the atmosphere pose a serious challenge to the climate and human health. In this study, we produced a dataset through merging the Moderate Resolution Imaging Spectrometers (MODIS) Collection 6.1 3-km resolution Dark Target aerosol optical depth (DT AOD) with the 10-km resolution Deep Blue aerosol optical depth (DB AOD) data by linear regression and made use of it to unravel the spatiotemporal characteristics of aerosols over the Pan Yangtze River Delta (PYRD) region from 2014 to 2017. Then, the geographical detector method and multiple linear regression analysis were employed to investigate the contributions of influencing factors. Results indicate that: (1) compared to the original Terra DT and Aqua DT AOD data, the average daily spatial coverage of the merged AOD data increased by 94% and 132%, respectively; (2) the values of four-year average AOD were high in the north-east and low in the south-west of the PYRD; (3) the annual average AOD showed a decreasing trend from 2014 to 2017 while the seasonal average AOD reached its maximum in spring; and that (4) Digital Elevation Model (DEM) and slope contributed most to the spatial distribution of AOD, followed by precipitation and population density. Our study highlights the spatiotemporal variability of aerosol optical depth and the contributions of different factors over this large geographical area in the four-year period, and can, therefore, provide useful insights into the air pollution control for decision makers.