Abstract. The purpose of this study is to develop an emission inventory for major anthropogenic air pollutants and VOC species in the Yangtze River Delta (YRD) region for the year 2007. A "bottom-up" methodology was adopted to compile the inventory based on major emission sources in the sixteen cities of this region. Results show that the emissions of SO 2 , NO x , CO, PM 10 , PM 2.5 , VOCs, and NH 3 in the YRD region for the year 2007 are 2392 kt, 2293 kt, 6697 kt, 3116 kt, 1511 kt, 2767 kt, and 459 kt, respectively. Ethylene, mp-xylene, o-xylene, toluene, 1,2,4-trimethylbenzene, 2,4-dimethylpentane, ethyl benzene, propylene, 1-pentene, and isoprene are the key species contributing 77 % to the total ozone formation potential (OFP). The spatial distribution of the emissions shows the emissions and OFPs are mainly concentrated in the urban and industrial areas along the Yangtze River and around Hangzhou Bay. The industrial sources, including power plants other fuel combustion facilities, and non-combustion processes contribute about 97 %, 86 %, 89 %, 91 %, and 69 % of the total SO 2 , NO x , PM 10 , PM 2.5 , and VOC emissions. Vehicles take up 12.3 % and 12.4 % of the NO x and VOC emissions, respectively. Regarding OFPs, the chemical industry, domestic use of paint & printing, and gasoline vehicles contribute 38 %, 24 %, and 12 % to the ozone formation in the YRD region.

Abstract. Regional trans-boundary air pollution has become an important issue in the field of air pollution modeling. This paper presents the results of the implementation of the MM5-CMAQ modeling system in the Yangtze River Delta (YRD) for the months of January and July of 2004. The meteorological parameters are obtained by using the MM5 model. A new regional emission inventory with spatial and temporal allocations based on local statistical data has been developed to provide input emissions data to the MM5-CMAQ modeling system. The pollutant concentrations obtained from the MM5-CMAQ modeling system have been compared with observational data from the national air pollution monitoring network. It is found that air quality in winter in the YRD is generally worse than in summer, due mainly to unfavorable meteorological dispersion conditions. In winter, the pollution transport from Northern China to the YRD reinforces the pollution caused by large local emissions. The monthly average concentration of SO 2 in the YRD is 0.026 ± 0.011 mg m −3 in January and 0.017 ± 0.009 mg m −3 in July. Monthly average concentrations of NO 2 in the YRD in January and July are 0.021 ± 0.009 mg m −3 , and 0.014 ± 0.008 mg m −3 , respectively. The monthly average concentration of PM 10 in the YRD is 0.080 ± 0.028 mg m −3 in January and 0.025 ± 0.015 mg m −3 in July. Visibility is also a problem, with average deciview values of 26.4 ± 2.95 dcv in winter and 17.6 ± 3.3 dcv in summer. The ozone concentration in the downtown area of a city like Correspondence to: C. H. Chen (chench@saes.sh.cn) Zhoushan can be very high, with the highest simulated value reaching 0.24 mg m −3 . In January, the monthly average concentration of O 3 in the YRD is 0.052 ± 0.011 mg m −3 , and 0.054 ± 0.008 mg m −3 in July. Our results show that ozone and haze have become extremely important issues in the regional air quality. Thus, regional air pollution control is urgently needed to improve air quality in the YRD.

A high O₃ episode was detected in urban Shanghai, a typical city in the Yangtze River Delta (YRD) region in August 2010. The CMAQ integrated process rate method is applied to account for the contribution of different atmospheric processes during the high pollution episode. The analysis shows that the maximum concentration of ozone occurs due to transport phenomena, including vertical diffusion and horizontal advective transport. Gas-phase chemistry producing O₃ mainly occurs at the height of 300–1500 m, causing a strong vertical O₃ transport from upper levels to the surface layer. The gas-phase chemistry is an important sink for O₃ in the surface layer, coupled with dry deposition. Cloud processes may contribute slightly to the increase of O₃ due to convective clouds or to the decrease of O₃ due to scavenging. The horizontal diffusion and heterogeneous chemistry contributions are negligible during the whole episode. Modeling results show that the O₃ pollution characteristics among the different cities in the YRD region have both similarities and differences. During the buildup period, the O₃ starts to appear in the city regions of the YRD and is then transported to the surrounding areas under the prevailing wind conditions. The O₃ production from photochemical reaction in Shanghai and the surrounding area is most significant, due to the high emission intensity in the large city; this ozone is then transported out to sea by the westerly wind flow, and later diffuses to rural areas like Chongming island, Wuxi and even to Nanjing. The O₃ concentrations start to decrease in the cities after sunset, due to titration of the NO emissions, but ozone can still be transported and maintain a significant concentration in rural areas and even regions outside the YRD region, where the NO emissions are very small

No abstract

SARS-CoV-2 rapidly spreads among humans via social networks, with social mixing and network characteristics potentially facilitating transmission. However, limited data on topological structural features has hindered in-depth studies. Existing research is based on snapshot analyses, preventing temporal investigations of network changes. Comparing network characteristics over time offers additional insights into transmission dynamics. We examined confirmed COVID-19 patients from an eastern Chinese province, analyzing social mixing and network characteristics using transmission network topology before and after widespread interventions. Between the two time periods, the percentage of singleton networks increased from 38.9 % to 62.8 % ðp < 0:001Þ; the average shortest path length decreased from 1.53 to 1.14 ðp < 0:001Þ; the average betweenness reduced from 0.65 to 0.11 ðp < 0:001Þ; the average cluster size dropped from 4.05 to 2.72 ðp ¼ 0:004Þ; and the out-degree had a slight but nonsignificant decline from 0.75 to 0.63 ðp ¼ 0:099Þ: Results show that nonpharmaceutical interventions effectively disrupted transmission networks, preventing further disease spread. Additionally, we found that the networks' dynamic structure provided more information than solely examining infection curves after applying descriptive and agent-based modeling approaches. In summary, we investigated social mixing and network characteristics of COVID-19 patients during different pandemic stages, revealing transmission network heterogeneities.