Summer Pollution Research Articles

Association analysis between socioeconomic factors and urban ozone pollution in China.

Ozone pollution in China has gradually increased, attracting extensive attention. Existing studies on ozone pollution typically take environmental and chemical perspectives. As air pollution is closely related to social and economic activities, it is also important to study ozone pollution from a socioeconomic perspective. Using the association rule mining technique, we uncovered hidden patterns between ozone variance and socioeconomic factors in macro-, meso-, and micro-scenarios in 297 Chinese cities. We found that the acceleration of urbanization and industrialization has indeed aggravated urban ozone pollution. The supply of water and power resources may be a significant factor influencing urban ozone pollution. Transportation hub cities with more developed economies and industries are more likely to suffer from ozone pollution in summer and autumn. Human behavior is a critical factor influencing the weekly variance in ozone concentration during weekdays and weekends. The influence of plant-derived VOC emissions on the formation of ozone cannot be overlooked. Our results deepen the understanding of ozone pollution in Chinese cities, and we provide corresponding policy recommendations to alleviate ozone pollution and improve air quality.

Spatio-Temporal Prediction of Ground-Level Ozone Concentration Based on Bayesian Maximum Entropy by Combining Monitoring and Satellite Data

Ozone (O3) pollution is one of the predominant environmental problems, and exposure to high O3 concentrations has a significant negative influence on both human health and ecosystems. Therefore, it is essential to analyze spatio-temporal characteristics of O3 distribution and to evaluate O3 exposure levels. In this study, O3 monitoring and satellite data were used to estimate O3 daily, seasonal and one-year exposure levels based on the Bayesian maximum entropy (BME) model with a spatial resolution of 1 km × 1 km in the Beijing-Tianjin-Hebei (BTH) region, China. Leave-one-out cross-validation (LOOCV) results showed that R2 for daily and one-year exposure levels were 0.81 and 0.69, respectively, and the corresponding values for RMSE were 19.58 μg/m3 and 4.40 μg/m3, respectively. The simulation results showed that the heavily polluted areas included Tianjin, Cangzhou, Hengshui, Xingtai, and Handan, while the clean areas were mainly located in Chengde, Qinhuangdao, Baoding, and Zhangjiakou. O3 pollution in summer was the most severe with an average concentration of 134.5 μg/m3. In summer, O3 concentrations in 87.7% of the grids were more than 100 μg/m3. In contrast, winter was the cleanest season in the BTH region, with an average concentration of 51.1 μg/m3.

Characteristics and Source Apportionment of Volatile Organic Compounds (VOCs) in a Typical Industrial Area in Dongguan During Periods of Ozone and Non-ozone Pollution in Summer

To investigate the characteristics and sources of atmospheric volatile organic compounds (VOCs) in a typical industrial zone in Dongguan, 56 VOCs species were continuously measured in Houjie Town of Dongguan in summer of 2020. In addition, mass concentrations of O3, NOx, and CO and meteorological data were synchronously collected. Then, characteristics of total VOCs and major species, the contributions of major VOCs species to ozone formation potential (OFP), and source apportionment of VOCs under the different ozone concentrations were discussed. The mean mixing ratio of VOCs was 53.1×10-9 including aromatics (24.7×10-9), alkanes (23.7×10-9), alkenes (3.9×10-9), and alkynes (0.7×10-9). The mean mixing ratios of aromatics, alkanes, alkenes, and alkynes increased approximately 10%, 43%, 38%, and 98% during the period of ozone pollution, respectively, compared with those during the period of non-ozone pollution. Aromatics contributed the most to OFP during the periods of both ozone pollution and non-ozone pollution, followed by alkanes, alkenes, and alkynes. Solvent sources, liquefied petroleum gas (LPG) leakage, fossil fuel combustion, and hydrocarbon volatilization were resolved using the PMF model, which accounted for 60%±20%, 16%±11%, 15%±11%, and 9%±6% of total VOCs, respectively. During the period of ozone pollution, the contribution of solvent sources to the total VOCs decreased to 44%, whereas that of LPG leakage and hydrocarbon volatilization increased to 21% and 16%, respectively.

Chemical Reaction Activity and Source Apportionment of Atmospheric VOCs in Summer in Dalian

To investigate the characteristics and source apportionment of atmospheric volatile organic compounds (VOCs) in Dalian, the concentration level, composition characteristics, reaction activity, and source apportionment of atmospheric VOCs in Dalian were analyzed based on high-resolution online observation VOCs data from June to August 2020. The results showed that the average φ(VOCs) was (10.21±5.71)×10-9, in which alkanes accounted for 66.35%, alkenes for 11.89%, alkynes for 7.75%, and aromatics for 14.01%. VOCs and NOx were high at night and low during the day, whereas the change trend of O3 was opposite. Considering the species activity, it was determined that toluene, ethylene, m/p-xylene, 1-hexene, n-butane, isopentane, n-pentane, and isoprene were the key species affecting atmospheric VOCs in Dalian. Priority control of olefin and aromatic hydrocarbon emissions is the key to improve O3 pollution in summer in Dalian. By applying the positive matrix factorization (PMF) model, six major VOCs sources were extracted, namely traffic sources (26.38%), combustion sources (22.75%), industrial emission sources (17.09%), solvent usage sources (14.59%), natural sources (11.72%), and others (7.47%). The emissions of traffic sources and combustion sources are the key pollution sources for O3 prevention and control in Dalian in summer.

One-year Simulation of Air Pollution in Central China, Characteristics, Distribution, Inner Region Cross-transmission, and Pathway Research in 18 Cities

Air pollution in Henan province is serious and is significantly impacted by pollution transmission and interactions with surrounding areas. The emission sources in 18 cities in Henan province were labeled and applied to the WRF-CMAQ traceability model for simulation in January, April, July, and October of 2017. The pollutant distribution results showed that due to the combined influence of emissions and meteorology, the concentrations of PM2.5, NO2, and SO2 in Henan province were the highest in winter and the lowest in summer. The seasonal variation in O3-8h concentration was the highest in summer, followed by spring, and the lowest in winter. There was a large difference in pollutant concentrations between different seasons. The average concentrations of PM2.5, NO2, and SO2 in winter in Henan province were 4.17, 4.12, and 6.24 times those in summer, respectively, whereas the concentration of O3-8h in summer was 2.24 times that in winter. Since PM2.5, NO2, and SO2 are closely related to primary emissions and have a certain homology, the distributions of high values of these three pollutants were higher in the north and lower in the south, and the seasonal trends were more consistent. The seasonal distribution of O3-8h varied widely, with high O3-8h values mainly distributed in the northeastern region of Henan province in summer, when meteorological conditions contributed to O3 production; in winter, spring, and autumn, high O3-8h values were mainly distributed in the southern part of Henan province due to the suppression of meteorological conditions and NOx consumption. The results of the study on the transport of pollutants showed that extra-provincial transport and natural sources contributed the most to the concentrations of PM2.5, O3-8h, NO2, and SO2 in winter, with 36.20%-72.32%, 77.96%-96.08%, 49.45%-78.80%, and 59.05%-88.85%, respectively. When considering only local emissions and intra-provincial transmission, the contributions of emissions to local concentrations of the four pollutants in summer were the highest in all cities of Henan province. The contributions of intra-provincial transmission to PM2.5 and O3-8h in spring were the largest, with 25.63%-74.69% and 30.21%-80.01%, respectively, and the contributions of intra-provincial transmission to NO2 and SO2 in winter were larger, with 26.02%-76.96% and 20.30%-82.34%. The transmission paths of PM2.5, NO2, and SO2 were more similar in Henan province, with more transmission from north to south in winter, from west to east and southwest to northeast in spring, from southwest to northeast in summer, and from north to south in autumn; however, the transmission of PM2.5 was more complicated. The O3-8h transport path was more different from the others, especially in autumn when pollutants were mostly transported from north to south, but the O3-8h transport path from southwest to northeast was obvious.

Study of Heavy Metal Analysis, Concentration, and Correlation of Particulate Matter

In this present paper we studied about the analysis, concentration, and correlation of particulate matter. Concentration of a dozen of elements namely Cd, Cr, Cu, Mn, Ni, Pb, Zn, Fe, Ca, K, Na and Al were quantified. Sampling was done on nuclear polycabonate filters. For principal component analysis (PCA) of elements and mass concentration data, three main sources were identified as PM2.5 and PM2.5-10 concentration. They are resuspended crustal dust, industrial emissions and vehicles exhaust. The effect of weather conditions was also studied. It was found that pollution in summer was higher than those in winter. The PM2.5/PM2.5-10 ratio in summer was found to be in the range of 0.35 - 0.9 whereas the same ratio in winter was 0.20 - 0.50.

Spatial-Temporal Distribution and Variation of NO2 and Its Sources and Chemical Sinks in Shanxi Province, China

In present China, continuing to control PM2.5 (particulate matter < 2.5 μm) and preventing the rise of O3 are the most urgent environmental tasks in its air clean actions. Considering that NO2 is an important precursor of PM2.5 and O3, a comprehensive analysis around this pollutant was conducted based on the real-time-monitoring data from Jan 2018 to Mar 2019 in 11 prefecture-level cities in Shanxi Province of China. The results showed that the annual average concentration of NO2 in Shanxi prefecture-level cities is mainly distributed in the range of 28.84–48.93 μg/m3 with the values in five cities exceeding the Chinese Grade Ⅱ standard limit (40 μg/m3). The over-standard days were all concentrated in the heating season with a large pollution peak occurring in winter except in Lvliang, while four cities also had a small pollution peak in summer. High NO2 polluted areas were mainly concentrated in the central part of Shanxi, and trended on the whole from the southwest to the northeast (Lvliang/Linfen—Taiyuan/Jinzhong—Yangquan/Jinzhong), which was different from the spatial distribution of PM2.5 and O3. Lvliang was the hot spot of NO2 pollution in summer, while Taiyuan was the hot spot in winter. Concentration Weighted Trajectory (CWT) analysis indicated that central-north Shaanxi, central-south Shanxi, northern Henan, the south of Shijiazhuang and areas around Erdos in Inner Mongolia were important source areas of NO2 in Shanxi besides local emissions. Our findings are expected to provide valuable implications to policymakers in Shanxi of China to effectively abate the air pollution.

Aerosol Optical Characteristics with Ground-based Measurements via Sun Photometer and Its Relationship with PM Particle Concentration in Chang'an

The simultaneous quantitative investigation of aerosol ground observation data and particle concentration data is important for a better understanding of the vertical distribution characteristics of air pollution and formulating reasonable air pollution control measures in Chang'an, Xi'an. CE-318 measurements from October 2018 to April 2021 were systematically analyzed to reveal the seasonal and yearly variations in atmospheric aerosols in Chang'an. Then, the relationship between AOD and particle concentration in different seasons and different pollution degrees was explored. The obtained results were as follows:① the seasonal variation in AOD in Chang'an was determined, whereby autumn (1.02)>winter (1.00)>summer (0.63)>spring (0.47). Distinct monthly and inter-annual differences in AOD were observed, showing that the annual average of AOD in 2019 was higher than that in 2020. ② Obvious seasonal and monthly differences in aerosol main control modes were observed; the dominant mode of aerosols gradually changed from coarse mode to fine mode from spring to winter. The seasonal variation in the main control mode of aerosols in 2019 was similar to that in the whole observation period. The seasonal aerosol Angstrom wavelength index (Angstrom) was evenly distributed in 2020, and aerosol particles existed in coarse mode form. Generally speaking, the aerosol type in Chang'an was mostly mixed aerosol throughout the whole observation period. ③ Significant seasonal variations in the relationship between AOD and Angstrom was shown, in which the air pollution in spring was dominated by coarse mode aerosol particles. The local pollution in summer was caused by coarse and fine mode aerosol particles, and the fine mode particles were dominant when the pollution was obvious. The characteristic distribution of Angstrom in autumn and winter was similar; in case of local pollution, the coarse mode aerosol particles were dominant, and in case of obvious pollution, the fine mode aerosol particles were dominant. ④ The monthly concentration variation trend of PM2.5 and PM10 in Chang'an consistent with the maximum and minimum values appeared in January and summer, respectively. Seasonal variation in the concentration of PM2.5 and PM10 was apparent, with the highest values in winter. ⑤ A positive correlation between AOD and particulate matter concentration in different seasons and pollution levels was obtained during the observation periods, but the correlation coefficient differed. The specific characteristics were as follows:the correlation between AOD and PM2.5 concentration was greater than that with PM10 concentration; the correlation between AOD and particle concentration in autumn and winter was greater than that in spring and summer; and the correlation between AOD and particulate matter concentration was greater in polluted weather. Particle concentration was the most important determinant of AOD change, followed by relative humidity, which was the meteorological factor with the highest interpretation rate of AOD change in Chang'an. This indicates that a higher correlation between AOD and particle concentration would benefit from a reasonable humidity correction of AOD.

Organic Aerosols and Source Analysis of Fine Particles in the Background of Shiwanda Mountain, Guangxi

The non-polar compounds in atmospheric fine particles (PM2.5) mainly include polycyclic aromatic hydrocarbons (PAHs) and normal alkanes (n-alkanes), etc., which are usually used to identify the source of the air pollution and have a great important impact on human health and the environment. To study the pollution characteristics and sources of non-polar organic aerosols in the background point PM2.5 in Guangxi, from November 2017 to October 2018, 17 types of PAHs and 20 types of n-alkanes were analyzed on PM2.5 samples collected in the field. It was found that the annual average concentrations of PAHs and n-alkanes were 4.28±4.25 ng·m-3 and 13.7±14.72 ng·m-3, respectively. The seasonal change was as follows:winter[(7.86±5.19) ng·m-3, (27.51±16.90) ng·m-3]>spring[(2.73±1.76) ng·m-3, (7.64±4.71) ng·m-3]>autumn[(2.34±145) ng·m-3, (7.01±4.55) ng·m-3]>summer[(1.91±1.67) ng·m-3, (3.98±3.12) ng·m-3]. In PAHs, 5-ring and 6-ring molecules accounted for more than 60%, followed by low- and medium-ring molecules (4-ring and 3-ring). The high molecular weight of n-alkanes was relatively high (C29>C31>C27), and the odd and even carbon numbers were significantly different. In addition, combined with the feature ratio method, principal component analysis method, and backward trajectory joint verification, it was found that 41.5% of non-organic aerosols in winter were affected by maritime traffic emissions and ocean source transportation, and 36.7% of the pollution was explained by the coal burning and local biomass burning; 25.2% of the pollution in spring came from biomass combustion and transportation, and 45.0% was attributed to marine transportation in the southern part of the study area and higher plant wax emissions pollution; 53.4% of pollution in summer came from polluted ship emissions, and 10.6% of pollution came from transportation in Southwest Thailand Source transportation. The organic aerosols at the background sites in Guangxi were affected by the combined effects of local emissions and transmission sources.

Pollution Characteristics and Source Apportionment of Atmospheric Volatile Organic Compounds in Summer in Yuncheng City

Based on the online monitoring data of VOCs, O3, and NO2 in Yuncheng City from June to August 2020, the pollution characteristics of VOCs in Yuncheng City in summer were analyzed. At the same time, the main emission sources were determined using a PMF model, and the chemical reactivity of VOCs was evaluated using the maximum incremental reactivity (MIR) method and fractional aerosol coefficients (FAC). The results showed that the urban area of Yuncheng was seriously polluted by VOCs and NO2 in the early morning and evening during summer, the peak value of VOCs daily variation occurred at 08:00 and 20:00, respectively, and was mainly affected by the morning and evening peaks in traffic. The ρ(VOCs) from June to August was 50.52 μg·m-3, and the species with the highest proportion were alkanes (39.39%) and oxygenated volatile organic compounds (OVOCs, 34.63%). Five VOCs emission sources were determined by the PMF model, of which the largest contribution was from motor vehicle exhaust emission sources (33.10%), followed by industrial emission sources (29.46%), natural gas and coal combustion sources (17.31%), solvent use sources (11.94%), and plant emission sources (8.19%). Controlling motor vehicle exhaust emission sources is the key to alleviate VOCs pollution in summer in Yuncheng City. The average ozone formation potential (OFP) of VOCs was 162.88 μg·m-3, in which OVOCs had the highest contribution rate (45.37%); acetaldehyde, propionaldehyde, ethylene, isoprene, and toluene were the key active components; and industrial emission sources were the emission sources with the highest contribution rate. The average value of secondary organic aerosol formation potential (SOAp) of VOCs was 0.40 μg·m-3, in which the contribution rate of aromatic hydrocarbons was the highest (88.00%), and the solvent use source was the emission source with the highest contribution rate.

Chemical Characterization of Fine and Coarse Particulate Matter in Gopalganj District of Bihar

A sampling campaign was conducted in Gopalganj district of Bihar for assessing the mass concentration of PM2.5 and PM2.5-10 so as to correlate it with the air quality of the district. Concentration of a dozen of elements namely Cd, Cr, Cu, Mn, Ni, Pb, Zn, Fe, Ca, K, Na and Al were quantified. Sampling was done on nuclear polycabonate filters. For principal component analysis (PCA) of elements and mass concentration data, three main sources were identified as PM2.5 and PM2.5-10 concentration. They are resuspended crustal dust, industrial emissions and vehicles exhaust. The effect of weather conditions was also studied. It was found that pollution in summer was higher than those in winter. The PM2.5/PM2.5-10 ratio in summer was found to be in the range of 0.35 - 0.9 whereas the same ratio in winter was 0.20 - 0.50. The concentration of both types of particles exceeded the permissible WHO limits.

Memory Behaviors of Air Pollutions and Their Spatial Patterns in China

Particulate matter (PM2.5 and PM10) and ozone (O3) are the two major air pollutants in China in recent years. The fluctuations of PM2.5, PM10 and O3 strongly depend on the weather processes and anthropogenic emission. These processes may lead to the existence of short- and long-term memory behaviors in air pollutants. Hence, here we use the autoregressive parameter a of the first-order autoregressive process [AR (1)] to characterize the short-term memory effects of pollutants. We estimate the scaling exponent α using detrended fluctuation analysis (DFA) for the long-term memory effects of air pollutants (PM2.5, PM10, and O3) in summer and winter for different cities in China. Our results show that PM2.5, PM10, and O3 have strong short-term and long-term memory characteristics both in summer and winter. Furthermore, both the short- and long-term memory effects are stronger in winter than summer for most cities associated with stronger and longer persistent weather systems in winter. In general, the scaling exponent α of PM2.5 and PM10 are smaller for northern cities than those of southern cities in China. The long-term memory patterns of O3 are stronger in northern cities and weaker in southern cities in relative to those of PM2.5 and PM10 in winter. Our results show that the short- and long-term memory behaviors of air pollutions are dominated by the weather systems with different time scales.

Analysis and Forecast of Beijing’s Air Quality Index Based on ARIMA Model and Neural Network Model

Based on Beijing’s Air Quality Index (AQI) and concentration changes of the six major pollutants from 2019 to 2021, the results are visualized through descriptive statistics, and the air pollution status and influencing factors of Beijing’s AQI are analyzed using the ARIMA model and neural network. A forecast system is built and the fitting effects of the two models are compared. The results show that PM2.5, PM10, and O3 of the six major pollutants have the greatest impact on AQI. Beijing’s air quality now shows a trend of improvement in recent years; however, there is obvious seasonal evidence that the summer pollution index has been high. Therefore, special attention should be paid to the treatment of ozone pollution in summer. Both models are useful for the forecast of AQI, but the forecast effect of the neural network model is better than that of the ARIMA model. Moreover, when using the additive seasonal model for the long-term forecast of monthly data, it is found that the Beijing AQI still shows seasonal cyclicality and has a slightly decreasing trend in the next two years. This research provides a basis for the forecast of air quality and policy enlightenment for environmental protection departments to deal with air pollution.

Household PM2.5 pollution in rural Chinese homes: Levels, dynamic characteristics and seasonal variations

Humans generally spend most of their time indoors, and fine particulate matter (PM2.5) in indoor air can have seriously adverse effects on human health due to the long exposure time. This study conducted field measurements to explore seasonal variations of PM2.5 concentrations in household air by revisiting the same rural homes in southern China and factors influencing indoor PM2.5 concentrations were explored mainly by one-way ANOVA. The PM2.5 concentrations of outdoor, kitchen and living room air were 38.9 ± 12.2, 47.1 ± 20.3 and 50.8 ± 24.1 μg/m3 in summer, respectively, which were 2.3 to 2.9 times lower than those in winter (p < 0.05). The lower indoor PM2.5 pollution in summer was attributed to the transition to clean household energy and better ventilation. Fuel type can significantly affect PM2.5 concentrations in the kitchen, with greater PM2.5 pollution associated with wood combustion than electricity. Our study firstly found mosquito coil emission was an important contributor to PM2.5 in the living room of rural households, which should be investigated further. Dynamic variations of PM2.5 suggested that cooking, heating and mosquito coil emission can rapidly increase indoor PM2.5 concentrations (up to one order of magnitude higher than baseline values), as well as the indoor/outdoor PM2.5 ratios. This study had the first insight of seasonal differences of household PM2.5 in the same rural homes using real-time monitors, confirming the different patterns and characteristics of household PM2.5 pollution in different seasons.

Analysis of Air Pollution around a CHP Plant: Real Measurements vs. Computer Simulations

This study examines the concentrations of air pollution in the vicinity of a combined heat and power plant (CHP) and a communication route, using computer modeling of pollutant dispersion and spatial analysis based on real measurements in the city of Łódź, Poland, Europe. The research takes into account the concentrations of particulate matter (PM10, PM2.5, PM1.0) and gaseous pollutants (SO2 and VOC) in winter and summer. The spatial distribution of pollutants is discussed, including the presence of areas with increased accumulations of pollutants. Because atmospheric air has no natural boundaries, when analyzing any location, not only local sources of pollution, but also background pollution, should be analyzed. A clear difference was observed between the concentrations of pollutants in the summer and winter seasons, with significantly higher concentrations in the winter (heating) period. The impacts of road transport, individual heating systems, and combined heat and power plants were also assessed. Computer calculations confirmed that road transport accounted for the largest share of both PM and SO2 emissions. The CHP plant was responsible for the smallest percentage of dust emissions and was the next largest producer of SO2 emissions. The share of the total emissions from the individual sources were compared with the results of detailed field tests. The numerical analysis of selected pollution sources in combination with the field analysis shows that the identified pollution sources included in the analysis represent only a part of the total observed pollutant concentrations (suggesting that other background sources account for the rest).

Impacts of vehicle emission on air quality and human health in China

The growing of vehicle population aggravates air pollution and threatens human health. In this study, based on the refined whole-process vehicle emission inventory considering volatile organic compounds (VOCs) evaporation emission, the CAMx model was applied to comprehensively quantify the impacts of the vehicle sector on the annual and seasonal concentrations of PM2.5 and O3 in China. Also, the health risks caused by long-term exposure to PM2.5 and O3 were evaluated. The model results showed that vehicle emission was an important source of severe O3 pollution in summer, with a contribution of more than 30% in most parts of China, but not an important source of serious PM2.5 pollution in winter, with a contribution of less than 20% in heavily polluted regions in China. Compared to tailpipe emission, vehicle VOCs evaporation emission led to increases of 25% and 47% to sectoral contribution to PM2.5 and O3. Health risk assessment results showed that attributable deaths caused by long-term exposure to PM2.5 and O3 were 975,029 and 46,043 in 2018, to which vehicle emission contributed approximately 12.5% and 22.2%, respectively.

Impacts of Meteorological Factors, VOCs Emissions and Inter-Regional Transport on Summer Ozone Pollution in Yuncheng

Summer ozone (O3) pollution in China has become increasingly serious in recent years. This study is based on hourly data of near-surface ozone (O3) and nitrogen oxides (NOx) and volatile organic compounds (VOCs) from June to August 2020 in Yuncheng, combined with meteorological data to analyse the characteristics of O3 pollution in summer and the influence of meteorological factors, precursors, and long-range transport on O3 pollution. In this paper, the VOCs/NOx characteristic ratio method was used to explore the sensitivity of O3 generation. Backward trajectories, cluster analysis, potential source contribution factor (PSCF) analysis and concentration weight trajectory (CWT) analysis were also calculated using Trajstat software. In 2020, Yuncheng had persistent O3 pollution, with the highest concentrations in June, significantly higher than July and August. Conditions of high temperature, low relative humidity and low wind speed contribute to the O3 accumulation. VOCs are the main precursors to the local production of O3. Besides, the long-range transport analysis shows that southeast-oriented air masses are the main direction influencing summer O3 pollution. The primary potential source areas of O3 are in the central and southern part of Henan province, the north-western Anhui province, and the northern Shaanxi. In addition, northern Hubei and southwestern Shandong also influence O3 pollution in summer Yuncheng.

Responses of surface O3 and PM2.5 trends to changes of anthropogenic emissions in summer over Beijing during 2014–2019: A study based on multiple linear regression and WRF-Chem

Owing to the implementation of air pollution control actions, anthropogenic emissions in Beijing have changed in recent years. Understanding the impact of changes in anthropogenic emissions on O3 and PM2.5 trends is helpful for developing air quality management strategies. Herein, we investigated the variations of air pollutants in summer over Beijing using long-term data sets from 2014 to 2019, and explored the responses of O3 and PM2.5 trends to changes in anthropogenic emissions based on multiple linear regression (MLR) analysis and WRF-Chem model. The results indicated a significant decrease in PM2.5, but a near constant level of O3 during 2014–2019. The decrease rate of PM2.5, which was lower than that of SO2, might be due to the effect of NO2 on atmospheric PM2.5. Both the slightly increasing correlations between PM2.5 and NO2 and the WRF-Chem model simulations implied that atmospheric PM2.5 in Beijing is trending to be more sensitive to NOx than SO2. The emissions of NOx and VOCs from industry and transportation were found to make great contribution to O3 production in Beijing. Due to the titration of NOx in VOC-limited regime, the relatively low emission ratios of NOx and VOCs from industry and transportation in Beijing provided convincing evidence for the persistently high O3 concentrations during 2014–2019. However, the noticeable increase of the O3 trends in other areas (e.g., Hebei, Tianjin) could be explained by the significant decline in the emission ratios of NOx and VOCs from anthropogenic emissions especially industry during 2014–2019. Controlling the emission of NOx can substantially reduce PM2.5 pollution, but may aggravate O3 pollution, and thus effective VOC emission control strategies need to be considered for simultaneously controlling O3 and PM2.5 pollution in Beijing and other regions of China.

Mechanism of haze pollution in summer and its difference with winter in the North China Plain

Heavy haze pollution usually occurs in winter. However, according to the enhanced atmospheric boundary layer (ABL) field experiments conducted in the North China Plain (NCP) from 17 June to 6 July 2019, heavy haze pollution may also occur in summer, although with a lower probability. Winter haze pollution is significantly affected by adverse boundary layer meteorological conditions, whereas our study shows different mechanisms of summer haze pollution from that of winter. In summer, PM2.5 is distributed uniformly as a thick layer at a lighter pollution level; however, the PM2.5 column content in summer exceeds that in winter, suggesting that the better air quality in summer is mainly due to improved diffusion conditions. In summer, even under haze conditions, the ABL can develop over 1000 m and has a large ventilation similar to clean periods, which indicates both favourable vertical diffusion conditions and advection capability of the summer ABL. Unlike in winter, the heavy haze pollution in summer is often caused by regional transport which is related to local circulation. To explore the influence of different scale systems on summer haze pollution, we applied the spectral analysis method to surface PM2.5 concentrations. Strong periodicity of PM2.5 concentrations is found in 4–9 d and 1 d, corresponding to the impacts of large-scale synoptic system changes and the ABL evolution, respectively. The influence of weather change is much stronger than that of the ABL evolution on PM2.5 concentrations in summer. The resulting changes in PM2.5 concentrations are approximately 45 μg/m3 and 15 μg/m3, respectively. There has been a consensus on the importance of emission control in winter. And this study shows that heavy haze pollution can also occur in summer and regional joint emission control should also be emphasized in summer.

Improving Comfort and Health: Green Retrofit Designs for Sunken Courtyards during the Summer Period in a Subtropical Climate

The sunken courtyard has long been used in underground spaces and provides an important outdoor environment. It introduces natural elements to create a pleasant space for human activities. However, this study measured a typical sunken courtyard and found potential problems of excessive solar radiation and accumulated air pollutants in summer when at an acceptable outdoor temperature for human activities. To improve the comfort and health of a sunken courtyard, this research proposes some green retrofit designs. Firstly, compared with green wall, water and a tree, sunshade is a primary measure to improve thermal comfort. Combining sunshade, a green wall and water reduces the temperature by up to 5.6 °C in the activity zone during the hottest hour. Secondly, blocking/guiding wind walls can effectively improve the wind environment in a sunken courtyard, but only when the wind direction is close to the prevailing wind. A blocking wind wall was better at affecting velocity and uniformity, while the guiding wind wall was more efficient at discharging air pollutants. This study initially discusses the climate-adaptive design of underground spaces in terms of green, thermal comfort and natural ventilation. Designers should generally integrate above/underground and indoor/outdoor spaces using natural and artificial resources to improve comfort and health in underground spaces.