Evaluation of the Street Canyon Level Air Pollution Distribution Pattern in a Typical City Block in Baoding, China.

Urban traffic pollution, which is strongly influenced by the complex urban morphology, has posed a great threat to human health. In this study, we performed a high-resolution simulation of traffic pollution in a typical city block in Baoding, China, based on the Parallelized Large-eddy simulation Model (PALM), to examine the distribution patterns of traffic-related pollutants and explore their relationship with urban morphology. Based on the model results, we conducted a multi-linear regression (MLR) analysis and found that the distribution of air pollutants inside the city block was dominated by both traffic emissions and urban morphology, which explained about 70% of the total variance in spatial distribution of air pollutants. Excluding the contribution of emissions, over 50% of the total variance can still be explained by the urban morphology. Among these urban morphological factors, the key factors determining the spatial distribution of air pollution are "Distance from the road" (DR), "Building Coverage Ratio" (BCR) and "Aspect Ratio" (H/W) of the street canyon. Specifically, urban areas with lower Aspect Ratio, lower BCR and larger DR are less affected by traffic pollution. Compiling these individual factors, we developed a complex Urban Morphology Pollution Index (UMPI). Each unit increase in UMPI is associated with a one percent increase of nearby traffic pollution contribution. This index can help urban planners to semi-quantitatively evaluate building groups which tend to trap or ventilate traffic pollution and thus help to reduce human exposure to street canyon level pollution through either traffic emission control or urban morphology amelioration.

Analysis of wintertime O3 variability using a random forest model and high-frequency observations in Zhangjiakou-an area with background pollution level of the North China Plain.

The short-term health effects of ozone (O3) have highlighted the need for high-temporal-resolution O3 observations to accurately assess human exposure to O3. Here, we performed 20-s resolution observations of O3 precursors and meteorological factors to train a random forest model capable of accurately predicting O3 concentrations. Our model performed well with an average validated R2 of 0.997. Unlike in typical linear model frameworks, variable dependencies are not clearly modelled by random forest model. Thus, we conducted additional studies to provide insight into the photochemical and atmospheric dynamic processes driving variations in O3 concentrations. At nitrogen oxides (NOx) concentrations of 10-20 ppb, all the other O3 precursors were in states that increased the production of O3. Over a short timescale, nitrogen dioxide (NO2) can almost track each high-frequency variation in O3. Meteorological factors play a more important role than O3 precursors do in predicting O3 concentrations at a high temporal resolution; however, individual meteorological factors are not sufficient to track every high-frequency change in O3. Nevertheless, the sharp variations in O3 related to flow dynamics are often accompanied by steep temperature changes. Our results suggest that high-temporal-resolution observations, both ground-based and vertical profiles, are necessary for the accurate assessment of human exposure to O3 and the success and accountability of the emission control strategies for improving air quality.

High-resolution simulation of local traffic-related NOx dispersion and distribution in a complex urban terrain.

Urban air pollution features large spatial and temporal variations due to the high heterogeneity in emissions and ventilation conditions, which render the pollutant distributions in complex urban terrains difficult to measure. Current urban air pollution models are not able to simulate pollutant dispersion and distribution at a low computational cost and high resolution. To address this limitation, we have developed the urban terrain air pollution (UTAP) dispersion model to investigate, at a spatial resolution of 5 m and a temporal resolution of 1 h, the distribution of the local traffic-related NOx concentration at the pedestrian level in a 1 × 1 km2 area in Baoding, Hebei, China. The UTAP model was shown to be capable of capturing the local pollution variations in a complex urban terrain at a low computational cost. We found that the local traffic-related NOx concentration along or near major roads (10-200 µg m-3) was 1-2 orders of magnitude higher than that in places far from roads (0.1-10 µg m-3). Considering the background pollution, the NO and NO2 concentrations exhibited similar patterns with higher concentrations in street canyons and lower concentrations away from streets, while the O3 concentration exhibited the opposite behavior. Sixty percent of the NOx concentration likely stemmed from local traffic when the background pollution level was low. Both the background wind speed and direction substantially impacted the overall pollution level and concentration variations, with a low wind speed and direction perpendicular to the axes of most streets identified as unfavorable pollutant dispersion conditions. Our results revealed a large variability in the local traffic-related air pollutant concentration at the pedestrian level in the complex urban terrain, indicating that high-resolution computationally efficient models such as the UTAP model are required to accurately estimate the pollutant exposure of urban residents.

A combined Arctic-tropical climate pattern controlling the inter-annual climate variability of wintertime PM2.5 over the North China Plain.

In recent years, the Chinese government has made tremendous efforts to reduce the emissions of atmospheric pollutants throughout the country. An apparent improvement in air quality was observed in Beijing and its adjacent region during the winter of 2017/2018. However, caution should be taken in directly attributing this improvement to air control actions without taking the effects of climate variability into account. Here, we develop a statistical prediction model that can successfully predict the variability of wintertime PM2.5 concentrations observed over these regions. Our analysis indicates that the remarkable decrease in PM2.5 concentrations over the North China Plain (NCP) observed during the winter of 2017/2018 can be largely explained by changes in meteorological conditions. To clarify which climate factors control the inter-annual variability of wintertime PM2.5 pollution over the NCP, we further reconstructed a 30-year time series of wintertime PM2.5 levels over the NCP over the period of 1988-2017 using our statistical model. Through our analysis, we found that the combined Arctic-tropical climate effects related to the ENSO and Arctic warming controlled the inter-annual variability of wintertime PM2.5 over the NCP. Specifically, the rapid warming of the Barents-Kara Sea region enhances the Siberian High and thus plays an important role in improving the air quality over the NCP during the 2017/2018 wintertime. These results help us understand the role of climate variability in modulating air quality, especially its contributions to the winter of 2017/2018. These results may assist in the evaluation of current air control actions and the revision of relevant policy for the future, which are urgently needed for China.