Department of Earth and Environmental Sciences, Indian Institute of Science Education and Research Bhopal, Bhopal Bypass Road, Bhauri, Bhopal, Madhya Pradesh, 462 066, India.
Center for Research on Environment and Sustainable Technologies, Indian Institute of Science Education and Research Bhopal, Bhopal Bypass Road, Bhauri, Bhopal, Madhya Pradesh, 462 066, India.
Sci Rep. 2021 Jan 12;11(1):544. doi: 10.1038/s41598-020-79229-7.
Surface PM concentrations are required for exposure assessment studies. Remotely sensed Aerosol Optical Depth (AOD) has been used to derive PM where ground data is unavailable. However, two key challenges in estimating surface PM from AOD using statistical models are (i) Satellite data gaps, and (ii) spatio-temporal variability in AOD-PM relationships. In this study, we estimated spatially continuous (0.03° × 0.03°) daily surface PM concentrations using MAIAC AOD over Madhya Pradesh (MP), central India for 2018 and 2019, and validated our results against surface measurements. Daily MAIAC AOD gaps were filled using MERRA-2 AOD. Imputed AOD together with MERRA-2 meteorology and land use information were then used to develop a linear mixed effect (LME) model. Finally, a geographically weighted regression was developed using the LME output to capture spatial variability in AOD-PM relationship. Final Cross-Validation (CV) correlation coefficient, r, between modelled and observed PM varied from 0.359 to 0.689 while the Root Mean Squared Error (RMSE) varied from 15.83 to 35.85 µg m, over the entire study region during the study period. Strong seasonality was observed with winter seasons (2018 and 2019) PM concentration (mean value 82.54 µg m) being the highest and monsoon seasons being the lowest (mean value of 32.10 µg m). Our results show that MP had a mean PM concentration of 58.19 µg m and 56.32 µg m for 2018 and 2019, respectively, which likely caused total premature deaths of 0.106 million (0.086, 0.128) at the 95% confidence interval including 0.056 million (0.045, 0.067) deaths due to Ischemic Heart Disease (IHD), 0.037 million (0.031, 0.045) due to strokes, 0.012 million (0.009, 0.014) due to Chronic Obstructive Pulmonary Disease (COPD), and 1.2 thousand (1.0, 1.5) due to lung cancer (LNC) during this period.
需要进行表面 PM 浓度的评估研究。在没有地面数据的情况下，使用遥感气溶胶光学深度（AOD）来推导 PM。然而，使用统计模型从 AOD 估算地面 PM 存在两个关键挑战：（i）卫星数据空白，以及（ii）AOD-PM 关系的时空可变性。在这项研究中，我们利用印度中央邦（MP）2018 年和 2019 年的 MAIAC AOD 估算了空间连续（0.03°×0.03°）的日平均地面 PM 浓度，并将结果与地面测量结果进行了验证。利用 MERRA-2 AOD 填补了每日 MAIAC AOD 空白。然后，使用估算的 AOD 以及 MERRA-2 气象和土地利用信息，开发了一个线性混合效应（LME）模型。最后，使用 LME 输出开发了一个地理加权回归模型，以捕捉 AOD-PM 关系的空间可变性。在整个研究期间，模型化和观测 PM 之间的最终交叉验证（CV）相关系数 r 在 0.359 到 0.689 之间变化，而均方根误差（RMSE）在 15.83 到 35.85μg/m 之间变化。在研究期间，冬季（2018 年和 2019 年）PM 浓度（平均 82.54μg/m）最高，而季风季节（平均 32.10μg/m）最低，呈现出强烈的季节性。结果表明，MP 的 PM 平均浓度分别为 2018 年的 58.19μg/m 和 2019 年的 56.32μg/m，这可能导致总过早死亡人数为 0.106 万人（95%置信区间为 0.086 万至 0.128 万），其中包括 0.056 万人（95%置信区间为 0.045 万至 0.067 万）死于缺血性心脏病（IHD），0.037 万人（95%置信区间为 0.031 万至 0.045 万）死于中风，0.012 万人（95%置信区间为 0.009 万至 0.014 万）死于慢性阻塞性肺病（COPD），以及 1.2 千人（95%置信区间为 1.0 万至 1.5 万）死于肺癌（LNC）。
