Satellite-based estimation of hourly PM2.5 levels during heavy winter pollution episodes in the Yangtze River Delta, China.

In the developing countries such as China, most well-developed areas have suffered severe haze pollution, which was associated with increased premature morbidity and mortality and attracted widespread public concerns. Since ground-based PM2.5 monitoring has limited temporal and spatial coverage, satellite aerosol remote sensing data has been increasingly applied to map large-scale PM2.5 characteristics through advanced spatial statistical models. Although most existing research has taken advantage of the polar orbiting satellite instruments, a major limitation of the polar orbiting platform is its limited sampling frequency (e.g., 1-2 times/day), which is insufficient for capturing the PM2.5 variability during short but intense heavy haze episodes. As the first attempt, we quantitatively investigated the feasibility of using the aerosol optical depth (AOD) data retrieved by the Geostationary Ocean Color Imager (GOCI) to estimate hourly PM2.5 concentrations during winter haze episodes in the Yangtze River Delta (YRD). We developed a three-stage spatial statistical model, using GOCI AOD and fine mode fraction, as well as corresponding monitoring PM2.5 concentrations, meteorological and land use data on a 6-km modeling grid with complete coverage in time and space. The 10-fold cross-validation R2 was 0.72 with a regression slope of 1.01 between observed and predicted hourly PM2.5 concentrations. After gap filling, the R2 value for the three-stage model was 0.68. We further analyzed two representative large regional episodes, i.e., a "multi-process diffusion episode" during December 21-26, 2015 and a "Chinese New Year episode" during February 7-8, 2016. We concluded that AOD retrieved by geostationary satellites could serve as a new valuable data source for analyzing the heavy air pollution episodes.

The characteristics and local-regional contributions of atmospheric black carbon over urban and suburban locations in Shanghai, China.

Black carbon (BC), produced from the incomplete combustion of carbonaceous fuels, has emerged as a major contributor to global climate change with adverse health effects. Based on one-year (2016.06.01-2017.06.30) equivalent black carbon (eBC) measurements, this study analyzed the characteristics of eBC concentrations and the local-regional contributions at an urban site (Pudong, PD) and a suburban site (Qingpu, QP) in Shanghai, China. The results showed that the annual average eBC concentrations were 1.17 ±â€¯0.61 µg m-3 and 2.09 ±â€¯0.97 µg m-3 at PD and QP, respectively. The high eBC concentrations occurred in winter and at weekends both for PD and QP. There were significant negative correlation coefficients between the daily eBC, the daily wind speed (WS) and the daily boundary layer height (BLH) at PD (rws: 0.45, rblh = -0.35, p < 0.01) and QP (rws: 0.49, rblh = -0.32, p < 0.01). And the relative higher eBC concentrations coincided with southerly, southwesterly and westerly winds although these winds had lower frequencies. This could be related to the agricultural fire in these directions during summer harvesttime. The significant partial correlation coefficients of eBC-CO (ru:0.37-0.64, rs:0.18-0.44, p < 0.01) and eBC-NO2 (ru:0.49-0.74, rs:0.38-0.75, p < 0.01) could suggest that eBC mainly come from vehicular exhaust emissions in Shanghai. Besides, the higher eBC/PM2.5 (5.29% ±â€¯1.94%) and eBC/CO(0.30% ±â€¯0.14%) at QP indicated the more combustion activities and diesel-powered vehicle emissions in suburban areas. The concentration weighted trajectory (CWT) analysis indicated that the surrounding areas at the junction of Shanghai, Jiangsu, and Zhejiang provinces seemed to be relatively the most important sources outside of Shanghai.

Spatial characteristics and determinants of in-traffic black carbon in Shanghai, China: Combination of mobile monitoring and land use regression model.

Black carbon (BC) has emerged as a major contributor to global climate change. Cities play an important role in global BC emission. The present study investigated the spatial pattern of in-traffic BC at a high spatial resolution in Shanghai, the commercial and financial center in Mainland China. The determinants including road network, social economic status and point-source pollutants, which may influence the BC spatial variability were also discussed. From October to December 2016, mobile monitoring was conducted to assess the BC concentrations on three sampling routes in Shanghai with a total length of 116 km. The results showed that the mean in-traffic BC among three sampling routes was 10.77 ±â€¯3.50 µg/m3. BC concentrations showed a significant spatial heterogeneity. The highest BC concentrations were near industrial sources and that those high concentrations were associated with either direct emissions from the industries, freight traffic, or both. With the widely distributed polluting enterprises and high emitting vehicles, the average BC in the low urbanization areas (12.80 ±â€¯4.54 µg/m3) was 57% higher than that in the urban core (7.77 ±â€¯2.24 µg/m3). Furthermore, a land use regression (LUR) model based on mobile monitoring was developed to examine the determinants and its spatial variability of BC measurements which corresponded to 17 predictor variables, e.g. road network, land use, meteorological condition etc., in 7 buffer distances (100 m to 10 km). The variables of meteorological, socio-economical and the distance to BC point-sources were selected as the independent variables. It was found that the established LUR model could explain a proportion (68%) of the variability of BC. LUR modeling from mobile measurements was possible, but more work related to the effect of traffic regulation on BC could be helpful for informing best model practice.

Spatiotemporal Characteristics of Ecological Conditions and Its Response to Natural Conditions and Human Activities during 1990⁻2010 in the Yangtze River Delta, China.

The Yangtze River Delta (YRD) region, including Shanghai City and the Jiangsu and Zhejiang Provinces, is the largest metropolitan region in China. In the past three decades, the region has experienced an unprecedented process of rapid and massive urbanization, which has dramatically altered the landscape and detrimentally affected the ecological environments in the region. In this paper, we analyzed the spatiotemporal variations of ecological conditions (Eco_C) via a synthetic index with analytic hierarchy processes in the YRD during 1990⁻2010. The relative contributions of influencing factors, including two natural conditions (i.e., elevation (Elev) and land-sea gradient (Dis_coa)), three indicators of human activities (i.e., urbanization rate (Urb_rate), per capita GDP (Per_gdp), the percentage of secondary and tertiary industry employment (Per_ind)), to the total variance of regional Eco_C were also investigated. The results showed that: (1) The Eco_C over YRD region was "Moderately High", which was better than the national average and demonstrated obvious spatial variations between south and north. There existed fluctuations and an overall increasing trend for Eco_C during the study period, with 20% of the area being deteriorated and 40% being improved. (2) The areas with elevation below 10 m was relatively poor in Eco_C, while the regions above 1000 m showed the best Eco_C and had the most obvious changes (9.33%) during the study period. (3) The selected five influencing factors could explain 91.0⁻94.4% of the Eco_C spatial variability. Elevation was the dominant factor for about 42.4⁻52.9%, while urbanization rate and per capita GDP were about 32.5% and 9.3%.

[Spatial Simulation of Black Carbon Concentrations Based on a Land Use Regression Model and Mobile Monitoring over Shanghai, China].

Black carbon (BC) is an important component of atmospheric pollution and has significant impacts on air quality and human health. Choosing Shanghai city for a case study, this paper explores the statistical characteristics and spatial patterns of BC concentrations using a mobile monitoring method, which differs from traditional fixed-site observations. Land use regression (LUR) modeling was conducted to examine the determinants for on-road BC concentrations, e.g. population, economic development, traffic, etc. These results showed that the average on-road BC concentrations were (9.86±8.68) µg·m-3, with a significant spatial variation. BC concentrations in suburban areas[(10.47±2.04) µg·m-3] were 32.03% (2.54 µg·m-3) higher than those in the city center[(7.93±2.79) µg·m-3]. Besides, meteorological factors (e.g. wind speed and relative humidity) and traffic variables (e.g. the length of roads, distance to provincial roads, distance to highway) had significant effects on on-road BC concentrations (r:0.5-0.7, P<0.01). Moreover, the LUR model, including meteorological and traffic variables performed well (adjusted R2:0.62-0.75, cross validation R2:0.54-0.69, RMSE:0.15-0.20 µg·m-3), which demonstrates that on-road BC concentrations in Shanghai are mainly affected by these factors and traffic sources to some extent. Among them, the most accurate LUR model was developed with a 100 m buffer, followed by the LUR model with a 5 km buffer. This study is of great significance for the identification of spatial distribution patterns for on-road BC concentration and exploring their influencing factors in Shanghai, which can provide a scientific basis and theoretical support for simulating and predicting the response mechanisms of BC on human health and the natural environment.