Study On Spatial-temporal Variations And Influencing Factors Of Aerosols Optical Thickness &Air Pollutants In Guangdong And Its Core Urban Area Guangzhou And Shenzhen

As the strongest economic province, Guangdong has witnessed fast economic and industrial development, rapid urbanization and violent population expansion, and consequently high emission of air pollutants, deteriorating air quality and frequent occurrence of haze days, which causes serious damages to human health and ecological environment and has drawn close concerns from the government, the public and scientists. Taking Guangdong province and its core urban area-Guangzhou and Shenzhen as study areas, our research analyzed the variations of air quality and their affecting factors at different spatial and temporal scales and from different sources, by combining various datasets with numerical simulation.Firstly, we studied the spatio-temporal variations of aerosols and the driving factors in Guangdong during the period of 2010-2012, using satellite products of aerosol optical thickness(AOT), normalized differencevegetation index(NDVI), elevation and a variety of socio-economic factors. It is shown that higher values of AOT are located at the Pearl River Delta region and eastern Guangdong, like Chaozhou and Shantou, while lower values occur in the mountainous north and northeast. Seasonal variations of aerosols in Guangdong display as maximumvalues appearing in summer(0.416) and minimum values in autumn(0.254). Self-organizing maps and linear regression models are applied to quantitively analyze natural and anthropogenic factors affecting the distribution of aerosols. AOT is shown to be strongly negatively correlated to NDVI and elevation, and positively correlated with socio-economic factors, especially GDP, industry and vehicle density(R2 above 0.73), but not primary industry, illustrating that human activities have strong impacts on aerosols distribution in Guangdong Province.Multiple linear regression between AOT and the contributing factors showsmuch higher R2 values(>0.8), indicative of the influences on of complicated interactions among a variety of elements on aerosols.Then we adopted Glo BEIS(Global Biosphere Emission and Interactions System) to estimate the emission of biogenic VOCs(BVOCs) in Guangzhou. The results show that the total emission of BVOCs in 2012 is 4.39×104 ton, with emission of isoprene, monoterpene and other types of BVOCs being 2.44×104, 0.52×104 and 1.42×104, respectively. Local standard emission factors of vegetation, leaf area index, wind speed and humidity have obvious effects on the estimate results. The temporal variations of BVOCs show as highest in summer, especially in July and August, and lowest in winter. Lowest values of BVOCs are located at the old urban areas, such as Tianhe, Yuexiu, liwan and Haizhu districts, and higher values are located at the middle of Conghua, south-central Zengcheng, southern Panyu and Nansha district, while highest ones mainly occur in north-east Conghua, northern Zengcheng, the border of Huadu and Conghua, and the border of Huangpu and Baiyun,.We also discussed the effects of BVOCs on particle matters and ozone discussed through station measurements and satellite images. For the station in suburb, BVOCs contribute a lot to the formation of ozone and secondary particles. As to the whole Guangzhou, the spatial distribution of BVOCs is completely opposite to that of aerosols, while quite similar to that of ozone. It is inferred that BVOCs contribute little to secondary organic aerosols, but have a great effect on ozone in Guangzhou.Taking Shenzhen city as an example, we analyzed the temporal variations of station-measured PM10, PM2.5, SO2, NO2, CO and O3 concentrations during 2012-2014, and found out that all the pollutants display as higher in winter and lower in summer. Diurnal variations of PM10, PM2.5, NO2 and CO are double peaks and valleys, and the occurrence time of peaks is consistent with the rush hour traffic. Diurnal trends of SO2 and O3 are unimodal distribution. Besides, there are apparent differences in PM10, NO2 and CO concentrations on weekday and at weekends. The correlation coefficient between PM10 and PM2.5 is 0.96. Particle matters correlate significantly with SO2, NO2, CO and O3, and SO2 also correlates with NO2 and O3. However, there exists no correlation between NO2 and O3.Finaly we discussed the effects of meteorological factors on air pollutants w using station observations in Shenzhen. Relatively humidity, temperature, wind speed and precipitation are negatively correlated with concentrations of pollutants, while air pressure and hours of sunshine are positively correlated with concentrations of pollutants. The orders of influence are relative humidity> air pressure>precipitation> wind speed> hours of sunshine. The meteorological field and back trajectory in an event of continuous pollution in winter, 2013 were modeled using MM5(The Fifth-Generation NCAR/Penn Mesoscal Model) and HYsplit. It is found out that the pollution was mainly caused by the local emission of pollutants in the Pearl River Delta region under specific weather conditions.