基于卫星的全国性土地利用回归模型,用于估算印度各地每年的二氧化氮长期暴露量

IF 3.8 Q2 ENVIRONMENTAL SCIENCES
Neha Singh , Joe Van Buskirk , Sagnik Dey , Luke D. Knibbs
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引用次数: 0

摘要

在印度,二氧化氮(NO2)地面测量数据的匮乏是估算长期暴露和相关健康影响的一大挑战。本研究旨在开发和验证 2019 年印度全国范围的年度二氧化氮暴露模型,并确定将非连续(人工)测量值与参考级连续测量值一起纳入模型交叉验证是否会提高模型的预测能力。我们使用了一种有监督的前向添加线性回归方法,以拟合利用多达 804 个中央污染控制委员会地面监测站(n = 157 个连续监测站,n = 647 个人工监测站)和 209 个空间预测变量(包括卫星对流层二氧化氮柱)开发的土地利用回归模型。我们利用标准诊断和交叉验证 (CV) 方法建立了两个模型:一个仅使用连续监测站点,另一个使用连续监测站点和人工监测站点。我们还评估了最终模型残差的克里格化是否降低了空间自相关性并改善了模型 CV 结果。表现最佳模型的 LUR 系数被应用于 2015-2021 年的预测因子,并以 100 米为网格来估算人口加权暴露量。仅连续站点模型以及连续站点和人工站点组合模型的 CV-R2 值分别为 0.59(均方根误差 [RMSE]:9.4 μg/m3)和 0.54(均方根误差:8.3 μg/m3),且均包含卫星 NO2 预测因子。克里格化残差将综合模型的 CV-R2 提高到 0.70(均方根误差:7.2 μg/m3),但对连续地点模型没有任何改进。2019 年全国人口加权二氧化氮平均值为 22.1 μg/m3。我们估计,超过 92% 的印度人口每年暴露在超过世界卫生组织空气质量准则(10 μg/m3)的二氧化氮中。在德里、孟买和加尔各答,估计分别有 45%、100% 和 100%的人口的二氧化氮年浓度超过了印度标准(40 微克/立方米)。据我们所知,这是首个专门针对印度的长期二氧化氮 LUR 模型,感兴趣的研究人员可以使用该模型进行预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

National, satellite-based land-use regression models for estimating long-term annual NO2 exposure across India

National, satellite-based land-use regression models for estimating long-term annual NO2 exposure across India

In India, scarcity of ground-based measurements of nitrogen dioxide (NO2) is a major challenge for estimating long-term exposure and associated health impacts. This study aimed to develop and validate a national-scale annual NO2 exposure model for India for 2019 and determine if model cross-validation predictive ability was improved by including non-continuous (manual) measurements along with reference-grade, continuous measurements.

We used a supervised forward-addition linear regression method to fit land use regression (LUR) models developed with up to 804 Central Pollution Control Board ground monitoring stations (n = 157 continuous, n = 647 manual) and 209 spatial predictor variables, including satellite-based tropospheric NO2 columns. Two models were developed: one using continuous sites only and one using continuous and manual sites, with standard diagnostics and cross-validation (CV) methods. We also assessed if the kriging of final model residuals reduced spatial autocorrelation and improved model CV results. LUR coefficients for the best-performing model were applied to predictors for 2015–2021 and gridded at 100 m to estimate population-weighted exposure.

The continuous sites-only model and combined continuous and manual sites models had CV-R2 values of 0.59 (root-mean-square error [RMSE]: 9.4 μg/m3) and 0.54 (RMSE: 8.3 μg/m3), respectively, and both included the satellite NO2 predictor. Kriging residuals increased the CV-R2 of the combined model to 0.70 (RMSE: 7.2 μg/m3) but offered no improvement for the continuous site model. National population-weighted average NO2 was 22.1 μg/m3 in 2019. We estimated over 92% of the Indian population was exposed to annual NO2 exceeding the WHO air quality guideline (10 μg/m3). In Delhi, Mumbai, and Kolkata, an estimated 45%, 100%, and 100% of the population, respectively, experienced annual NO2 levels that surpassed Indian standards (40 μg/m3). To our knowledge, this is the first such long-term NO2 LUR model specific to India, and predictions are available to interested researchers.

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来源期刊
Atmospheric Environment: X
Atmospheric Environment: X Environmental Science-Environmental Science (all)
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