Black carbon (BC) is an important component of airborne fine particulate matter, with significant impacts on global climate change and human health. Taking Minhang District of Shanghai as the study area, a microaethalometer (MA200) and GPS were installed on the electric taxi to form a mobile observation platform to identify the spatial distribution and hot spots of atmospheric BC in urban environment. We analyzed the sources and influencing factors of BC. The results showed that the overall characteristics of the spatial distribution pattern of near surface atmospheric BC in Minhang District of Shanghai were high in the north and low in the south. The average BC concentration was (4.11±4.87) μg·m-3. The average concentrations of BC in working days and non-working days were (4.22±1.49) and (3.52±2.26) μg·m-3. The variability of BC concentration in the high value area was large, indicating that the increases of BC concentration in mobile observation were related to traffic accidents in the road section. In addition to human activities, large-scale dense vegetation might inhibit BC diffusion. The Absorption ngström Exponent (AAE) was (0.82±0.54), which was closer to that of fossil fuel combustion. The contributions of fossil fuel emissions, biomass combustion, and mixed sources to BC sources were 67.5%, 4.9% and 27.6%, respectively.

Air pollution has seriously endangered human health and the natural ecosystem during the last decades. Air quality monitoring stations (AQMS) have played a critical role in providing valuable data sets for recording regional air pollutants. The spatial representativeness of AQMS is a critical parameter when choosing the location of stations and assessing effects on the population to long-term exposure to air pollution. In this paper, we proposed a methodological framework for assessing the spatial representativeness of the regional air quality monitoring network and applied it to ground-based PM2.5 observation in the mainland of China. Weighted multidimensional Euclidean distance between each pixel and the stations was used to determine the representativeness of the existing monitoring network. In addition, the K-means clustering method was adopted to improve the spatial representativeness of the existing AQMS. The results showed that there were obvious differences among the representative area of 1820 stations in the mainland of China. The monitoring stations could well represent the PM2.5 spatial distribution of the entire region, and the effectively represented area (i.e. the area where the Euclidean distance between the pixels and the stations was lower than the average value) accounted for 67.32% of the total area and covered 93.12% of the population. Forty additional stations were identified in the Northwest, North China, and Northeast regions, which could improve the spatial representativeness by 14.31%.

Air pollution is a serious global environmental problem, especially in developing countries. PM2.5 is a major air pollutant that poses critical risks in urban areas. In this study, we identified the influence of comprehensive urbanization on regional PM2.5 in the Yangtze River Delta (YRD), the largest metropolitan region in China, from 1998 to 2015. The impacts of four urbanization subsystems (Economic, Spatial, Demographic, and Social) on the spatiotemporal evolution of PM2.5 were investigated at the city level using a linear mixed effect model (LME). The annual average concentration of PM2.5 over the YRD increased during the study period, with rapid growth in the northern plains and slow growth in the mountainous south. The LME model showed good performance between the predicted and observed PM2.5, with an R2 value for 10-fold cross-validation reaching 0.87 and a regression coefficient of 0.88. Urban population, GDP ratio of secondary industry, built-up area, total road area, number of students in colleges and universities, and total retail sales of consumer goods all had positive associations with PM2.5 concentration, while the proportion of tertiary industry employment, GDP ratio of primary industry, forest area, and number of hospital beds all had negative associations. Economic had the strongest effect (44%) on PM2.5 early on (1998–2003), but over time the contribution of other subsystems gradually increased. The overall contributions of urbanization type to PM2.5 in the YRD were Spatial (33.10%) > Economic (26.76%) > Demographic (16.85%) > Social (12.72%). Our results help deepen the understanding of regional air pollution in China as well as its correlation with urbanization and its subsystems.

Black carbon (BC), an important component of atmospheric aerosols, has a great influence on regional and global radiation balance, climate and human health due to its small particle size, large specific surface area and radiation forcing. The long-term variation of atmospheric BC over China during 1980-2019 was investigated through MERRA-2 reanalysis data. MERRA-2 BC generally presented a good correlation (average R = 0.61) with 852 monthly samples from ground-based observations at 64 stations around China. In recent 40 years, the annual-averaged atmospheric BC concentration derived from MERRA-2 reanalysis data was 1.10 +/- 0.22 mu g/m(3), with an average annual growth rate of 1.52%. The monthly BC concentrations showed a "W-shaped trend. Based on the Mann-Kendall trend analysis, the BC concentration can be roughly divided into three stages: (1) the "low value" stage with slow growth rate (1.68%) (1980-1999, 0.91 +/- 0.10 mu g/m(3)), (2) the fluctuating "median value" stage with high growth rate (4.44%) (2000-2007, 1.28 +/- 0.13 mu g/m(3)), and (3) the "high value" stage with slow downtrend (-0.87%) (2008-2019, 1.32 +/- 0.06 mu g/m(3)). Peak times and multi-year average growth rates of BC concentration and emission were not synchronized. The spatial distribution, dividing by the Hu Line, a line dividing the population density and urbanization of China, formed three BC high-value areas in Sichuan Basin, Northern Henan area and Beijing-Tianjin-Hebei (BTH). The altitude where the concentration of BC increased fastest at an average annual growth rate of 3.47% during 1980-2019 was between 0 and 500 m. The growth rate of BC concentration was close to zero as the altitude increased. During the past 40 years, significant overall uptrends were detected in MERRA-2 BC concentration with Mann-Kendall trend analysis at pixel scale, especially during 1980-1999 and 2000-2007. This increasing trend was more obvious in the eastern Hu Line. Whereas, a downward trend was appeared in the plains and basins of southeast China during 2008-2019.

During 2020, the COVID-19 pandemic resulted in a widespread lockdown in many cities in China. In this study, we assessed the impact of changes in human activities on air quality during the COVID-19 pandemic by determining the relationships between air quality, traffic volume, and meteorological conditions. The megacities of Wuhan, Beijing, Shanghai, and Guangzhou were selected as the study area, and the variation trends of air pollutants for the period January-May between 2016 and 2020 were analyzed. The passenger volume of public transportation (PVPT) and the passenger volume of taxis (PVT) along with data on precipitation, temperature, relative humidity, wind speed, and boundary layer height were used to identify and quantify the driving force of the air pollution variation. The results showed that the change rates of fine particulate matter (PM2.5), NO2, and SO2 before and during the lockdown in the four megacities ranged from -49.9% to 78.2% (average: -9.4% +/- 59.3%), -55.4% to -32.3% (average: -43.0% +/- 9.7%), and - 21.1% to 11.9% (average: -10.9% +/- 15.4%), respectively. The response of NO2 to the lockdown was the most sensitive, while the response of PM2.5 was smaller and more delayed. During the lockdown period, haze from uninterrupted industrial emissions and fireworks under the effect of air mass transport from surrounding areas and adverse climate conditions was probably the cause of abnormally high PM2.5 concentrations in Beijing. In addition, the PVT was the most significant factor for NO2, and meteorology had a greater impact on PM2.5 than NO2 and SO2. There is a need for more national-level policies for limiting firework displays and traffic emissions, as well as further studies on the formation and transmission of secondary air pollutants. (C) 2021 Elsevier B.V. All rights reserved.

Based on the ground-based observations from seven atmospheric background stations during 2009 to 2018 in monsoon Asia (including BKT station in Indonesia, LLN and WLG stations in China, RYO and YON stations in Japan, TAP station in Republic of Korea, and UUM station in Mongolia), we analyzed the temporal and spatial variation of atmospheric CH4 concentration and its driving factors using harmonic model and maximal information-based nonparametric exploration. The results showed that the CH4 concentration in monsoon Asia varied from 1853.04 to 1935.61 nmol·mol-1, higher than that in Mauna Loa (MLO) station (1838.33 nmol·mol-1) in Hawaii, USA. The CH4 concentration decreased from north to south, with the highest value in TAP station (1935.61 nmol·mol-1) in Republic of Korea and RYO station (1907.19 nmol·mol-1) in Japan. The average seasonal amplitude at YON station in Japan was the largest (108.20 nmol·mol-1); while that at WLG station in China was the smallest (29.48 nmol·mol-1). The seasonal amplitude of TAP station in Republic of Korea changed faster at the rate of 4.49 nmol·mol-1·a-1. Except for WLG and TAP stations, CH4 concentrations were low in summer and high in winter. From the long-term perspective, the CH4 concentration at LLN (7.68 nmol·mol-1·a-1) and WLG (7.56 nmol·mol-1·a-1) stations in China exhibited the most obvious growth trend. Compared with wind speed, temperature and precipitation had greater impact on CH4 concentration, which were negatively associated with CH4 concentration. Local CH4 emission at some stations had a significant positive effect on CH4 concentration.