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基于 COVID-19 期间在秘鲁利马南部进行的限制措施前后 PM 预测的统计建模方法。

Statistical modeling approach for PM prediction before and during confinement by COVID-19 in South Lima, Perú.

机构信息

Universidad César Vallejo, Escuela de Ingeniería Ambiental, Lima, Peru.

Dirección General de Salud Ambiental, Lima, Peru.

出版信息

Sci Rep. 2022 Oct 6;12(1):16737. doi: 10.1038/s41598-022-20904-2.

DOI:10.1038/s41598-022-20904-2
PMID:36202880
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9537318/
Abstract

A total of 188,859 meteorological-PM[Formula: see text] data validated before (2019) and during the COVID-19 pandemic (2020) were used. In order to predict PM[Formula: see text] in two districts of South Lima in Peru, hourly, daily, monthly and seasonal variations of the data were analyzed. Principal Component Analysis (PCA) and linear/nonlinear modeling were applied. The results showed the highest annual average PM[Formula: see text] for San Juan de Miraflores (SJM) (PM[Formula: see text]-SJM: 78.7 [Formula: see text]g/m[Formula: see text]) and the lowest in Santiago de Surco (SS) (PM[Formula: see text]-SS: 40.2 [Formula: see text]g/m[Formula: see text]). The PCA showed the influence of relative humidity (RH)-atmospheric pressure (AP)-temperature (T)/dew point (DP)-wind speed (WS)-wind direction (WD) combinations. Cool months with higher humidity and atmospheric instability decreased PM[Formula: see text] values in SJM and warm months increased it, favored by thermal inversion (TI). Dust resuspension, vehicular transport and stationary sources contributed more PM[Formula: see text] at peak times in the morning and evening. The Multiple linear regression (MLR) showed the best correlation (r = 0.6166), followed by the three-dimensional model LogAP-LogWD-LogPM[Formula: see text] (r = 0.5753); the RMSE-MLR (12.92) exceeded that found in the 3D models (RMSE [Formula: see text]) and the NSE-MLR criterion (0.3804) was acceptable. PM[Formula: see text] prediction was modeled using the algorithmic approach in any scenario to optimize urban management decisions in times of pandemic.

摘要

共使用了 188859 份气象-PM[Formula: see text]数据进行验证，这些数据在 COVID-19 大流行之前（2019 年）和期间（2020 年）进行了验证。为了预测秘鲁利马南部两个地区的 PM[Formula: see text]，分析了数据的每小时、每日、每月和季节性变化。应用了主成分分析（PCA）和线性/非线性建模。结果表明，圣胡安市米拉弗洛雷斯（SJM）的年平均 PM[Formula: see text]最高（PM[Formula: see text]-SJM：78.7 [Formula: see text]g/m[Formula: see text]），圣地亚哥苏尔科（SS）的 PM[Formula: see text]最低（PM[Formula: see text]-SS：40.2 [Formula: see text]g/m[Formula: see text]）。PCA 显示了相对湿度（RH）-大气压（AP）-温度（T）/露点（DP）-风速（WS）-风向（WD）组合的影响。湿度较高且大气不稳定的凉爽月份降低了 SJM 的 PM[Formula: see text]值，而温暖月份则增加了 PM[Formula: see text]值，有利于热逆温（TI）。尘埃再悬浮、车辆运输和固定源在早晚高峰时段贡献了更多的 PM[Formula: see text]。多元线性回归（MLR）显示了最佳相关性（r = 0.6166），其次是三维模型 LogAP-LogWD-LogPM[Formula: see text]（r = 0.5753）；MLR 的 RMSE（12.92）超过了 3D 模型的 RMSE[Formula: see text]，MLR 的 NSE 标准（0.3804）可以接受。使用算法方法对 PM[Formula: see text]进行建模，以优化大流行期间的城市管理决策。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a2f/9537318/72a93f3ea40a/41598_2022_20904_Fig10_HTML.jpg
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