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Effects of urban lake wetlands on the spatial and temporal distribution of air PM10 and PM2.5 in the spring in Wuhan

机译:城市湖泊湿地对武汉春季春季空气PM10和PM2.5的空间和时间分布的影响

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Deposition of particulate matter (PM) from air is influenced by land use types and their attributes. Urban lakes wetlands can promote this deposition through their effects on the microclimate. To document these effects we investigated air PM10 and PM2.5 concentrations in transects around by monitoring PM and land use of 16 urban lake wetlands in the spring in Wuhan. The results showed that (1) lake wetland land-use regression models for air PM10,2.5 were successfully developed with an adjusted R-(PM10)(2) (0.365-0.982) and adjusted R-(PM2.5)(2) (0.483-0.969). Based on the lake wetland land use regression (LW LUR) model, the traffic variable was determined to be the strongest predictor of PM10,2.5, followed by distance to the city centre and relative humidity for PM10 and open green space and distance to the city centre for PM2.5. (2) The landscape descriptors of the lake wetland (less than 0.2 km(2) lake area) significantly affected PM10,2.5 in the central areas of the city, and air PM10,2.5 concentration was negatively correlated with wetland area (WA), landscape shape index (LSI), and proportion of non-built up area in the 500-m buffer zone surrounding the lake wetland (PB). (3) The lake wetlands showed decreasing air PM10,2.5 concentration, and the maximum air PM10,2.5 concentration gap between the wetlands and their surroundings occurred at 12:00-14:00 am and was from 10 to 50 mu g/m(3) for PM10 and from 5 to 15 mu g/m(3) for PM2.5. Effective understanding of these PM10,2.5 effects can contribute to better planning of urban development when considering the rising concerns of global air pollution and continued rapid urbanization.
机译:从空气中沉积颗粒物质(PM)受土地使用类型及其属性的影响。城市湖湖湿地可以通过它们对微气密的影响来促进这种沉积。要记录这些效果,我们通过监测武汉春季的春季中的16个城市湖泊湿地的PM和土地利用来调查Air PM10和PM2.5趋势。结果表明,用调整后的R-(PM10)(2)(0.365-0.982)和调整的R-(PM2.5)(2)成功开发出Air PM10.2.5的湖泊湿地土地利用回归模型(2)(2) (0.483-0.969)。基于湖泊湿地土地利用回归(LW LUR)模型,将交通变量决定为PM10,2.5的最强预测因子,其次是与市中心的距离和PM10的相对湿度,打开绿色空间和到城市的距离PM2.5中心。 (2)湿地的景观描述符(少于0.2公里(2)湖区)在城市中央地区的下方PM10,2.5显着影响,Air PM10,2.5浓度与湿地区域(WA)负相关,景观形状指数(LSI),以及湖泊湿地(PB)周围的500米缓冲区的非建筑面积的比例。 (3)湖泊湿地表现出浓度下降的空气PM10,2.5浓度,湿地和周围环境之间的最大空气PM10,2.5浓度差距为12:00-14:00 AM,为10至50μg/ m( 3)PM2.5的PM10和5至15μg/ m(3)。有效地了解这些PM10,2.5效果可以在考虑全球空气污染和持续快速城市化的关注时,有助于更好地规划城市发展。

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