Air Pollution In Beijing Research Articles

Assessing the Efficacy of Air Pollution Mitigation in Beijing: Insights from GIS and Machine Learning Analyses

This paper examines Beijing air pollution treatment through examining the Aerosol Optical Depth from 2013 to 2019, and pollutants density change in the region of Beijing city, Tianjin city, and Hebei province, using remote sensing products, i.e., aerosol optical depth from MODIS and the Sentinel- 5P SO2 and NO2 density products in Google Earth Engine and QGIS. The study finds that the whole region experienced an AOD decrease. In addition, the patterns suggest that Beijing didn’t shift its pollution to the surrounding area. However, Beijing city’s SO2 and NO2 density still remain high in the region. Additionally, a machine learning model is introduced to further understand the dynamics of PM2.5 in Beijing.

Time series analysis of PM2.5 pollution risk based on the supply and demand of PM2.5 removal service: a case study of the urban areas of Beijing.

Demonstrating the temporal changes in PM2.5 pollution risk in regions facing serious PM2.5 pollution problems can provide scientific evidence for the air pollution control of the region. However, research on the variation of PM2.5 pollution risk on a fine temporal scale is very limited. Therefore, we developed a method for quantitative characterizing PM2.5 pollution risk based on the supply and demand of PM2.5 removal services, analyzed the time series characteristics of PM2.5 pollution risk, and explored the reasons for the temporal changes using the urban areas of Beijing as the case study area. The results show that the PM2.5 pollution risk in the urban areas of Beijing was close between 2008 and 2012, decreased by approximately 16.3% in 2016 compared to 2012, and further decreased by approximately 13.2% in 2021 compared to 2016. The temporal variation pattern of the PM2.5 pollution risk in 2016 and 2021 showed significant differences, including an increase in the number of risk-free days, a decrease in the number of heavily polluted days, and an increase in the stability of the risk day sequence. The significant reduction in risk level was mainly attributed to Beijing's air pollution control measures, supplemented by the impact of COVID-19 control measures in 2021. The results of PM2.5 pollution risk decomposition indicate that compared to the previous 2years, the stability and predictability of the risk variation in 2016 increased, but the overall characteristics of high risk from November to February and low risk from April to September did not change. The high risk from November to February was mainly due to the demand for coal heating during this period, a decrease in PM2.5 removal service supply caused by plant leaf fall, and the common occurrence of temperature inversions in winter, which hinders the diffusion of air pollutants. This study provides a method for the analysis of PM2.5 pollution risk on fine temporal scales and may provide a reference for the PM2.5 pollution control in the urban areas of Beijing.

Pollution Characteristics and Source Apportionment of Black Carbon Aerosols during Spring in Beijing.

Black carbon (BC) aerosols are important for absorbing aerosols, affecting global climate change and regional air quality, and potentially harming human health. From March to May 2023, we investigated black carbon aerosol levels and air pollution in Beijing. Employing methods such as linear regression, Potential Source Contribution Function (PSCF) and Concentration-Weighted Trajectory (CWT), we analyzed the characteristics and sources of black carbon aerosols in the region. Results indicate that the light absorption coefficients of BC and BrC decrease with increasing wavelength, with BrC accounting for less than 40% at 370 nm. Daily variations in BC and PM2.5 concentrations exhibit similar trends, peaking in March, and BC displays a distinct bimodal hourly concentration structure during this period. Aethalometer model results suggest that liquid fuel combustion contributes significantly to black carbon (1.08 ± 0.71 μg·m-3), surpassing the contribution from solid fuel combustion (0.31 ± 0.2 μg·m-3). Furthermore, the significant positive correlation between BC and CO suggests that BC emissions in Beijing predominantly result from liquid fuel combustion. Potential source area analysis indicates that air masses of spring in Beijing mainly originate from the northwest (40.93%), while potential source areas for BC are predominantly distributed in the Beijing-Tianjin-Hebei region, as well as parts of the Shandong, Shanxi and Henan provinces. Moreover, this study reveals that dust processes during spring in Beijing have a limited impact on black carbon concentrations. This study's findings support controlling pollution in Beijing and improving regional air quality.

Influence of Urbanization on the Spatial Distribution of Associations Between Air Pollution and Mortality in Beijing, China.

This study investigated the influence of urbanization on the intra-city spatial distribution of associations between air pollution and mortality in Beijing, China. First, we utilized the generalized additive model to establish the exposure-response associations of PM2.5, O3, with nonaccidental and cardiorespiratory mortality between urban and suburban areas. Second, we assessed district-specific air pollution-related mortality and analyzed how these associations were affected by the degree of urbanization. Finally, we analyzed the changes in air pollution-related mortality before and after the enforcement of the Air Pollution Prevention and Control Action Plan (referred to as the Action Plan). The effect estimates of PM2.5 for nonaccidental mortality were 0.20% (95% CI: 0.12-0.28) in urban areas and 0.46% (95% CI: 0.35-0.58) in suburban areas per 10μg/m3 increase in PM2.5 concentrations. The corresponding estimates of O3 were 0.13% (95% CI: -0.04-0.29) in urban areas and 0.34% (95% CI: 0.12-0.56) in suburban areas per 10μg/m3 increase in O3 concentrations; however, the difference between the estimates of O3 in urban and suburban areas was not statistically significant. The district-specific results suggested that the estimated risks increased along with urban vulnerability levels for the effects of PM2.5. Implementing the Action Plan reduced the mortality risks of PM2.5, but the risks of O3 increased in some districts. However, the difference in the estimates between the pre- and post-emission reductions was not statistically significant. Our study indicated that populations living in less urbanized areas are more vulnerable to the adverse effects of air pollution in Beijing, particularly for PM2.5.

Lockdown effects of the COVID-19 on the spatio-temporal distribution of air pollution in Beijing, China

To prevent the spread of COVID-19, China enacted a series of strict policies, which reduced anthropogenic activities to a near standstill. This provided a precious window to explore its effects on the spatio-temporal distribution of air pollution in Beijing, China. In this study, continuous wavelet transforms and spatial interpolation methods were used to explore the spatiotemporal variations in air pollutants and their lockdown effects. The results indicate that except O3, the annual average concentration of NO2, PM2.5 and SO2 showed a decreasing trend during 2016 and 2019; NO2, PM2.5 and SO2 show a trend of “low in summer and high in winter”; the diurnal variation of NO2 concentration was mainly related to the rush hours of traffic volume, with the first peak at the morning peak (7:00), and then accumulating gradually to second peak (22:00). The continuous wavelet analysis shows that PM2.5, SO2 and NO2 had four primary periods, while O3 only had two primary periods. The high NO2 concentration areas were mainly in Dongcheng, Xicheng, Chaoyang and Fengtai, while the low concentration areas were located in the northern areas, such as Miyun and Huairou; the PM2.5 concentration decreased from south to north; this characteristic presented more obviously in winter. Compared to the pre-lockdown, NO2 and SO2 decreased considerably during lockdown, whereas PM2.5 and O3 increased dramatically. The contribution rates of transportation activities to the NO2, O3, PM2.5 and SO2 were estimated be 9.4 % ∼ 17.2 %, −76.4 % ∼ −42.9 %, −39.5 % ∼ –22.8 % and 5.7 % ∼ 43.7 %, respectively; the contribution rates of industrial activities were 19.9 % ∼ 26.7 %, 7.8 % ∼ 30.9 %, 1.6 % ∼ 36.2 % and −10.5 % ∼ 15.9 %, respectively. Considering meteorological factors, we inferred that pauses in anthropogenic activities indeed help improving air pollution, but it is difficult to offset the impact of extreme weather. These findings can enhance our understanding on the sources of air pollution, and can therefore provide insights on urban air pollution mitigation.

The Correlation Between Temperature Inversion and Atmosphere Pollution in Beijing

This study aimed to evaluate the correlation between temperature inversion and air pollution in Beijing, China in year 2020. The study first analyzed the monthly trend of temperature inversion levels in 2020, finding that it has two peaks in April and October and has the lowest value in August. The writer used line charts and linear regression method to study the correlation between temperature inversion level and the daily AQI level in Beijing in 2020. The study also focused on the relationship between concentration of polluting air particles: PM2.5, PM10, SO2, CO, NO2 and temperature inversion level, finding that temperature inversion level is positively correlated with the concentration of most polluting particles. The study provided comments for governments and citizens on how to reduce the effect of the air pollution caused by temperature inversion.

The impacts of urban anthropogenic heat and surface albedo change on boundary layer meteorology and air pollutants in the Beijing-Tianjin-Hebei region

In this study, present and future anthropogenic heat flux (AH) and surface albedo (SA) in Beijing are estimated and incorporated into the WRF-Chem/UCM model. A series of nested model simulations are conducted at 9 km and 27 km horizontal grid resolution with 30 layers in the vertical to explore the sensitivity of model results to urban surface characteristics during the study period from 23 February to 13 March 2014, and the impacts of anthropogenic heat and surface albedo changes in the future on urban meteorology and air pollutants in Beijing. Model validation demonstrates that the model with the up-to-date urban surface parameters apparently improves predictions for both meteorological variables and chemical species compared with model results with default urban parameters. The combined effect of SA increase and AH decrease leads to consistent decreases in T2 (air temperature at 2 m above ground), WS10 (wind speed at 10 m) and PBLH (planetary boundary layer height) by up to 1.2 °C, 0.4 m s−1 and 200 m (33%), respectively, in Beijing averaged over the study period. In response to the meteorological changes, near surface PM2.5 (particulate matter with aerodynamic equivalent diameter below 2.5 μm) concentration increases by up to 15.6 μg m−3 (8.6%), whereas O3 concentration decreases by 2.6 ppb (25.1%), respectively. In daytime, near surface cooling due to SA increase and AH decrease causes a strong downdraft over Beijing urban areas, a wind divergence near the surface and an upslope wind over the western mountain, resulting in aerosol transport from urban district to western mountain slope. At nighttime, surface cooling is weaker than that in daytime, leading to a weaker downdraft over Beijing and a slight downslope wind in the mountain. The combined changes in SA and AH lead to a decrease in T2 by 1.2 °C, a decrease of O3 concentration by approximately 20%, and increases in PM2.5 and its components by 10–17% in daytime averaged over the urban areas of Beijing during the study period, and the above changes are weaker during haze days than those in clean days. The changes in urban surface parameters in Beijing in the future may be benefit for mitigating heat wave and O3 pollution risks, but could increase fine aerosol level and human health risk, which require an optimal strategy for co-benefits of climate change mitigation and air pollution control in future city planning.

Retrieval of aerosol optical depth from GF-6 wide field of view 16-m data based on deep blue algorithm

Aerosol optical depth (AOD) is an important indicator for air pollution monitoring. We obtain AOD from the GF-6 satellite, which was launched on June 2, 2018 by China. Beijing is selected as the study area. Based on the deep blue algorithm, the moderate-resolution imaging spectroradiometer surface reflectance product is used to correct and establish the surface reflectance library, and the AOD is retrieved from the GF-6 wide field of view (WFV) blue band. So, the application of the deep blue algorithm in a GF-6 WFV camera is realized. The aerosol is divided into three models: a custom model, a continental model, and an urban model in second simulation of the satellite signal in the solar spectrum model. To improve the accuracy of retrieval results, we recalculated the aerosol component concentration ratio based on GF-6 WFV data and ground observation as the custom model. The retrieval results of the three aerosol models show that the central and southern urban areas are higher than the mountainous areas in the northwest, indicating that human emission is the main source of air pollution in Beijing. The retrieval results of the custom model, continental model, and urban model are compared with the ground observation. The error of urban model retrieval is large, and the determination coefficient R2 is only 0.3671. The results of the custom model and continental model are similar. The R2 of the custom is 0.8942, which is higher than that of the continental (0.8872). We analyze the relationship between the retrieved AOD of the three aerosol models and top of atmosphere (TOA) reflectance and find that the custom AOD has the best sensitivity to TOA reflectance, whereas the urban AOD has the worst sensitivity. So, the custom aerosol model is closer to the real aerosol situation in Beijing.

Measurement report: Ambient volatile organic compound (VOC) pollution in urban Beijing: characteristics, sources, and implications for pollution control

Abstract. The increasing ozone (O3) pollution and high fraction of secondary organic aerosols (SOA) in fine particle mass has highlighted the importance of volatile organic compounds (VOCs) in air pollution control. In this work, four intensive field measurements of VOCs during winter of 2018 (from 1 December 2018 to 17 January 2019), spring (15 April to 27 May), summer (17 June to 13 July), and autumn (22 September to 27 November) of 2019 were conducted at an urban site in Beijing to characterize VOC sources and their contributions to air pollution. The total mixing ratio of the 95 quantified VOCs (TVOC) observed in this study ranged from 5.5–118.7 ppbv with the mean value of 34.9 ppbv. Alkanes, OVOCs, and halocarbons were the dominant chemical groups, accounting for 75 %–81 % of the TVOC across the sampling months. The molar ratios of VOCs to NOx indicated that O3 formation was limited by VOCs during the whole sampling period. Positive matrix factorization (PMF) analysis showed that diesel vehicle exhaust, gasoline vehicle exhaust, and industrial emissions were the main VOC sources during both the O3-polluted and PM2.5-polluted months. On the basis of O3 formation impact, VOCs from fuel evaporation and diesel exhaust, particularly toluene, xylenes, trans-2-butene, acrolein, methyl methacrylate, vinyl acetate, 1-butene, and 1-hexene, were the main contributors, illustrating the necessity of conducting emission controls on these pollution sources and species to alleviate O3 pollution. Instead, VOCs from diesel exhaust as well as coal and biomass combustion were found to be the dominant contributors to secondary organic aerosol formation potential (SOAFP), particularly the VOC species of toluene, 1-hexene, xylenes, ethylbenzene, and styrene, and top priority should be given to these for the alleviation of haze pollution. This study provides insights for the government to formulate effective VOC control measures for air pollution in Beijing.

Heat stress in Beijing and its relationship with boundary layer structure and air pollution

Both extreme heat and heavy air pollution can cause adverse health impacts on urban inhabitants. To understand heat stress and its relationship with boundary layer structure and air pollution in Beijing, this study analyzed surface meteorological observations, radiosonde measurements, and ground-level PM2.5 and O3 concentrations in summer from 2015 to 2019, in conjunction with simulated air quality and MERRA-2 data. We measured the heat stress using a heat index that combines temperature and humidity to quantify the sensible temperature as perceived by humans, and found that high heat risk in Beijing was often associated with a low boundary layer height and poor air quality. To reveal the underlying physical mechanism involved, we objectively classified the synoptic conditions in North China. The typical synoptic pattern associated with the coupling of heat and pollution in Beijing was found to feature a southeast-to-north pressure gradient at the 700-hPa level, leading to westward warm advection above planetary boundary layer (PBL) and southward movement of warm, humid, and polluted air masses within the PBL towards Beijing. The elevated warm advection can enhance the capping of thermal inversion over the PBL and suppress the PBL's development and the vertical dispersion of pollutants. With mountains to the north and west, pollutants and heat can be trapped in a limited volume in Beijing, increasing the health risk from heat and pollution. These findings on the meteorological mechanisms of the coupling between heat and pollution in Beijing may have important implications for limiting the current health risk and preparing for any projected changes in it in the future.

Evaluation of the Influence between Local Meteorology and Air Quality in Beijing Using Generalized Additive Models

Previous studies have confirmed the inextricable connection between meteorological factors and air pollutants. This study presents the complex nonlinear relationship between meteorological variables and four major air pollutants under high-concentration air pollution in Beijing. The generalized additive model combined with marginal effects is used for quantitative analysis. After controlling the confounding factors such as long-term trends, seasonality and spatio-temporal deviation, the final fitting results exhibit that temperature, relative humidity and visibility are the most significant meteorological variables associating with PM2.5 concentration, and the marginal effect reaches 80%, −23% and 270%, respectively. Temperature and relative humidity are the most significant variables for SO2, and the marginal effect reaches 15% and 7%. The most significant variables for O3 are temperature and solar radiation, with marginal effect of up to 70% and 8%. Atmospheric pressure and temperature results in a positive effect on CO, and the marginal effect can reach 18% and 80%. All these indicate that local meteorological variables are a significant driving factor for air quality in Beijing. Other variables, such as wind speed, visibility, and precipitation, display some influence on air pollutants, but have less explanatory power in the model. Overall, our study provides a better understanding of the relationship between local meteorological variables and air quality, as well as an insight into how climate change affects air quality.

Association between blood pressure and short-term exposure to ambient air pollutants in Beijing, China

Substantial evidences have pointed to a link between hypertension and air pollution. This explored the link between blood pressure and short-term exposure to ambient atmospheric pollutants in a heavily polluted city. A monitoring program was designed to continuously record physical examination data of 1796 participants in Beijing, China from 2013 to 2018, and the individual-level exposure of atmospheric pollutants (PM2.5, PM10, NO2, CO, O3, and SO2) was estimated using land use regression model. A generalized additive mixed-effects model was applied within the investigation of the acute impacts of air pollutants on blood pressure, including systolic blood pressure, diastolic blood pressure, pulse pressure, and mean arterial pressure. Air pollutant concentrations were found to be positively correlated with blood pressure levels after their adjustment for a number of demographic and meteorological elements, e.g. each 10 μg/m3 increment of NO2 resulted in an increment in systolic blood pressure of 0.351 mm Hg (95th percentile confidence interval: 0.001, 0.701; p < 0.05). In addition, stronger associations were found among males and patients with hypertension, and the blood pressure level of outdoor workers was more susceptible to the changes in NO2 concentration. Generally, elevated concentrations of air pollutants contributed to increased levels of blood pressure.

Increasing impacts of the relative contributions of regional transport on air pollution in Beijing: Observational evidence

Benefiting from the pollution controls implemented by the Chinese government, the concentrations of PM2.5, SO2, NO2 and CO showed a significant decrease in Beijing during 2013–2017. In this study, an observation-based method was employed to estimate the relative contributions of regional transport (MaxRTC) and local emissions (MinLEC) to air pollutant levels during 2013–2017 in Beijing. The results showed that the MaxRTC to SO2 and PM2.5 increased significantly over the five years, while those to CO and NO2 changed little. Furthermore, the difference in the emissions control efficiency (ΔECE) between Beijing (receptor region) and Shijiazhuang (source region), which refers to the concentration changes corresponding to unit emission changes of a certain air pollutant between the two regions, was introduced to verify the estimated variation in MaxRTC and MinLEC over 2013–2017. The negative value of ΔECE found for PM2.5 and SO2 supports the conclusion of an increasing effect of regional transport. This implies that local emissions control alone is not adequate for mitigating Beijing's air pollution, especially with the demand for continuously improving air quality. Joint prevention and control with regard to air quality on a regional scale is more important and urgent in the next Five-Year Plan.

Using an Internet of Behaviours to Study How Air Pollution Can Affect People's Activities of Daily Living: A Case Study of Beijing, China.

This study aims to quantitatively model rather than to presuppose whether or not air pollution in Beijing (China) affects people’s activities of daily living (ADLs) based on an Internet of Behaviours (IoB), in which IoT sensor data can signal environmental events that can change human behaviour on mass. Peoples’ density distribution computed by call detail records (CDRs) and air quality data are used to build a fixed effect model (FEM) to analyse the influence of air pollution on four types of ADLs. The following four effects are discovered: Air pollution negatively impacts people going sightseeing in the afternoon; has a positive impact on people staying-in, in the morning and the middle of the day. Air pollution lowers people’s desire to go to restaurants for lunch, but far less so in the evening. As air quality worsens, people tend to decrease their walking and cycling and tend to travel more by bus or subway. We also find a monotonically decreasing nonlinear relationship between air quality index and the average CDR-based distance for each person of two citizen groups that go walking or cycling. Our key and novel contributions are that we first define IoB as a ubiquitous concept. Based on this, we propose a methodology to better understand the link between bad air pollution events and citizens’ activities of daily life. We applied this methodology in the first comprehensive study that provides quantitative evidence of the actual effect, not the presumed effect, that air pollution can significantly affect a wide range of citizens’ activities of daily living.

Characteristics and sources of volatile organic compounds during pollution episodes and clean periods in the Beijing-Tianjin-Hebei region

Volatile organic compounds (VOCs) play an important role in air pollution. In this study, we conducted comprehensive field observations to investigate wintertime air pollution in Beijing, Wangdu, and Dezhou in the Beijing-Tianjin-Hebei region during 2017 and 2018. The average VOC concentrations of the three sites were 35.6 ± 26.6, 70.9 ± 56.3, and 50.5 ± 40.0 ppbv, respectively. The species with the highest concentration were similar in all three sites and included ethane, ethylene, acetylene, acetone, and toluene. The VOC mixing ratios of the three sites showed synchronous growth during pollution episodes and were 1.2–2 times higher than those during clean periods. Moreover, the OH loss rates (LOH) during pollution episodes were 1.2–1.7 times that during clean periods. The crucial reactive species in the three sites were ethylene, propylene, and acetaldehyde, contributing approximately 70% to the total LOH during pollution periods. According to the source apportionment analysis, vehicle exhausts were the largest source of VOCs in Beijing, accounting for more than 50% of the total emissions. During the pollution episodes, Beijing's industrial emissions decreased, but the secondary and background sources increased. Coal combustion was significant (approximately 40%) in Wangdu and should therefore be prioritized in emission reduction policies. In Dezhou, industrial emissions had a considerable impact on the VOC mixing ratio during pollution periods and should therefore be prioritized. The backward trajectory analysis showed that VOCs from the southern region likely contribute to Beijing's VOC pollution, highlighting the importance of regional integration for air quality management.

Winter air quality improvement in Beijing by clean air actions from 2014 to 2018

In order to cope with heavy haze pollution in Beijing, the Air Pollution Prevention and Control Action Plan was issued in 2013. In this study, we took January observations from 2014 to 2018 as a case study to assess wintertime air quality improvement in Beijing after five years of continuous emission control measures and to investigate the causes of haze formation. SO2 and NO2 concentrations in January 2018 decreased by 84% and 41% from January 2014, while NH3 concentrations remained relatively stable during these years with an average of 7.28 μg m−3. The mean concentrations of PM2.5 and water-soluble inorganic ions declined over the study period, while compared with 2014, decreased SO42− ratio and increased NO3− contribution in PM2.5 were observed, highlighting the importance of reactive nitrogen (Nr) pollution control. High concentrations of NH3, NO2, as well as NH4+ and NO3− in PM2.5 at roadside monitoring sites, indicated that vehicle sources played a vital role in local urban Nr emissions. Finally, we found most NH3 existed in the gas phase in the Beijing atmosphere, even on polluted winter days, suggesting a considerable risk for additional secondary aerosol formation. Our findings demonstrate the effectiveness of the emissions control in alleviating Beijing's air pollution. Significant reductions of precursor emissions are needed to achieve the goals set by air quality standards in the future, especially for NH3. It is especially crucial to jointly consider and control multiple pollution sources to effectively reduce the high levels of air pollution that remain in Beijing.

Changes in air pollutants during the COVID-19 lockdown in Beijing: Insights from a machine-learning technique and implications for future control policy

The COVID-19 lockdowns led to abrupt reductions in human-related emissions worldwide and had an unintended impact on air quality improvement. However, quantifying this impact is difficult as meteorological conditions may mask the real effect of changes in emissions on the observed concentrations of pollutants. Based on the air quality and meteorological data at 35 sites in Beijing from 2015 to 2020, a machine learning technique was applied to decouple the impacts of meteorology and emissions on the concentrations of air pollutants. The results showed that the real (“deweathered”) concentrations of air pollutants (expect for O3) dropped significantly due to lockdown measures. Compared with the scenario without lockdowns (predicted concentrations), the observed values of PM2.5, PM10, SO2, NO2, and CO during lockdowns decreased by 39.4%, 50.1%, 51.8%, 43.1%, and 35.1%, respectively. In addition, a significant decline for NO2 and CO was found at the background sites (51% and 37.8%) rather than the traffic sites (37.1% and 35.5%), which is different from the common belief. While the primary emissions reduced during the lockdown period, episodic haze events still occurred due to unfavorable meteorological conditions. Thus, developing an optimized strategy to tackle air pollution in Beijing is essential in the future.摘要基于2015–2020年北京35个环境空气站和20个气象站观测资料, 应用机器学习方法 (随机森林算法) 分离了气象条件和源排放对大气污染物浓度的影响. 结果发现, 为应对疫情采取的隔离措施使北京2020年春节期间大气污染物浓度降低了35.1%–51.8%; 其中, 背景站氮氧化物和一氧化碳浓度的降幅最大, 超过了以往报道较多的交通站点. 同时, 2020年春节期间的气象条件不利于污染物扩散, 导致多次霾污染事件发生.为进一步改善北京空气质量, 未来需要优化减排策略.

Temporally resolved sectoral and regional contributions to air pollution in Beijing: informing short-term emission controls

Abstract. We investigate the contributions of local and regional emission sources to air pollution in Beijing to inform the design of short-term emission control strategies for mitigating major pollution episodes. We use a well-evaluated version of the WRF-Chem model at 3 km horizontal resolution to determine the daily accumulation of pollution over Beijing from local and regional sources in October 2014 under a range of meteorological conditions. Considering feasible emission reductions across residential, transport, power, and industrial sectors, we find that 1 d controls on local emissions have an immediate effect on PM2.5 (particulate matter with diameter less than 2.5 µm) concentrations on the same day but can have lingering effects as much as 5 d later under stagnant conditions. One-day controls in surrounding provinces have the greatest effect in Beijing on the day following the controls but may have negligible effects under northwesterly winds when local emissions dominate. To explore the contribution of different emission sectors and regions, we perform simulations with each source removed in turn. We find that residential and industrial sectors from neighbouring provinces dominate PM2.5 levels in Beijing during major pollution episodes but that local residential emissions and industrial or residential emissions from more distant provinces can also contribute significantly during some episodes. We then perform a structured set of perturbed emission simulations to allow us to build statistical emulators that represent the relationships between emission sources and air pollution in Beijing over the period. We use these computationally fast emulators to determine the sensitivity of PM2.5 concentrations to different emission sources and the interactions between them, including for secondary PM, and to create pollutant response surfaces for daily average PM2.5 concentrations in Beijing. We use these surfaces to identify the short-term emission controls needed to meet the national air quality target of daily average PM2.5 less than 75 µg m−3 for pollution episodes of different intensities. We find that for heavily polluted days with daily mean PM2.5 higher than 225 µg m−3, even emission reductions of 90 % across all sectors over Beijing and surrounding provinces may be insufficient to meet the national air quality standards. These results highlight the regional nature of PM pollution and the challenges of tackling it during major pollution episodes.

Is reducing new particle formation a plausible solution to mitigate particulate air pollution in Beijing and other Chinese megacities?

Atmospheric gas-to-particle conversion is a crucial or even dominant contributor to haze formation in Chinese megacities in terms of aerosol number, surface area and mass. Based on our comprehensive observations in Beijing during 15 January 2018-31 March 2019, we are able to show that 80-90% of the aerosol mass (PM2.5) was formed via atmospheric reactions during the haze days and over 65% of the number concentration of haze particles resulted from new particle formation (NPF). Furthermore, the haze formation was faster when the subsequent growth of newly formed particles was enhanced. Our findings suggest that in practice almost all present-day haze episodes originate from NPF, mainly since the direct emission of primary particles in Beijing has considerably decreased during recent years. We also show that reducing the subsequent growth rate of freshly formed particles by a factor of 3-5 would delay the buildup of haze episodes by 1-3 days. Actually, this delay would decrease the length of each haze episode, so that the number of annual haze days could be approximately halved. Such improvement in air quality can be achieved with targeted reduction of gas-phase precursors for NPF, mainly dimethyl amine and ammonia, and further reductions of SO2 emissions. Furthermore, reduction of anthropogenic organic and inorganic precursor emissions would slow down the growth rate of newly-formed particles and consequently reduce the haze formation.

Impact of residual layer transport on air pollution in Beijing, China.

The residual layer (RL) stores a large amount of pollutants, but its effect on near-surface pollution is unknown. In this study, a two-year continuous observation was performed in Beijing using a ceilometer. The generalized boundary layer includes the mixing layer and RL. The results showed that there is no significant seasonal difference in the generalized boundary layer height (GBLH). The average GBLHs in spring, summer, autumn and winter are 1155, 1139, 1036 and 1195m, respectively. The diurnal variation characteristics of spring, summer and autumn are similar, and the RL disappears when the mixing layer height reaches its peak in the afternoon. In winter, the development of the mixing layer is weak, and there is a 33.8% chance that the RL cannot be breached, thus making the mixing layer height at noon much lower than the GBLH. The concentrations of PM2.5 in the mixing layer and RL are 89 and 52μgm-3, respectively, and the probability that the PM2.5 concentration in the RL was higher than that near the ground was 38.9%. RL transport represents an important beginning of the pollution event during the winter mornings and afternoons in Beijing. This study is helpful to better understand the structure of the RL and its influence on air pollution.