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Satellite-based estimates of outdoor particulate pollution (PM10) for Agra City in northern India

机译:基于卫星的印度北部阿格拉市室外颗粒物污染(PM10)估算

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Air quality of north Indian cities has worsened over the last few decades which has been posing a great risk to consequential health-related issues. Ground-based monitoring of particulate matter smaller than 10 μm (PM10) in Indian cities has been limited to few selective sites at local hot spots, and thus, related health studies at regional scale were constrained. To overcome this issue, we utilized the aerosol optical depth (AOD) from Moderate Resolution Imaging Spectroradiometer (MODIS) onboard EOS Terra and Aqua satellites to estimate the regional PM10 concentration in Agra City located in the northern part of India. The approach envisaged the developments of linear, log-linear, and multi-linear regression models to estimate PM10 using AODMODIS and in situ measured meteorological parameters by utilizing the data of years 2010 and 2011. The results indicated that both hourly and 24-h average PM10 had a weak correlation with AODMODIS when chosen as the only regressor. However, hourly PM10 showed better correlation with AODMODIS (R ~0.45) than 24-h average PM10 (R ~0.24). The log-linear estimation of PM10 utilizing AODMODIS, relative humidity, wind speed, and atmospheric temperature as regressors had the highest correlation coefficient (R = 0.81) and a minimum relative standard error as 8.93 %, and thus, it was able to provide the best estimates of PM10 among all the models considered in this study. However, the model adequacy checks suggested the further scope of strengthening of these linear and log-linear models by adopting some suitable transformations in them.
机译:在过去的几十年中,印度北部城市的空气质量不断恶化,这对随之而来的健康相关问题构成了极大的风险。对印度城市中小于10μm(PM10)的颗粒物进行地面监测仅限于局部热点地区的少数选择性地点,因此,区域范围内的相关健康研究受到限制。为了解决这个问题,我们利用EOS Terra和Aqua卫星上的中分辨率成像光谱仪(MODIS)的气溶胶光学深度(AOD)来估算印度北部阿格拉市的区域PM10浓度。该方法设想开发线性,对数线性和多线性回归模型,以利用AODMODIS和通过利用2010年和2011年的数据现场测量的气象参数估算PM10。结果表明,每小时和24小时平均值当被选作唯一的回归因子时,PM10与AODMODIS的相关性较弱。但是,每小时PM10与AODMODIS(R〜0.45)的相关性高于24小时平均PM10(R〜0.24)。以AODMODIS,相对湿度,风速和大气温度作为回归因子的PM10的对数线性估计具有最高的相关系数(R = 0.81)和最小的相对标准误差为8.93%,因此,它能够提供在这项研究中考虑的所有模型中,PM10的最佳估计值。但是,模型适当性检查建议通过在模型中采用一些适当的变换来增强这些线性和对数线性模型的进一步范围。

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