| Autor: |
Rita Jaqueline Cabello-Torres, Manuel Angel Ponce Estela, Odón Sánchez-Ccoyllo, Edison Alessandro Romero-Cabello, Fausto Fernando García Ávila, Carlos Alberto Castañeda-Olivera, Lorgio Valdiviezo-Gonzales, Carlos Enrique Quispe Eulogio, Alex Rubén Huamán De La Cruz, Javier Linkolk López-Gonzales |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Scientific Reports, Vol 12, Iss 1, Pp 1-19 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2045-2322 |
| DOI: |
10.1038/s41598-022-20904-2 |
| Popis: |
Abstract A total of 188,859 meteorological-PM $$_{10}$$ 10 data validated before (2019) and during the COVID-19 pandemic (2020) were used. In order to predict PM $$_{10}$$ 10 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 $$_{10}$$ 10 for San Juan de Miraflores (SJM) (PM $$_{10}$$ 10 -SJM: 78.7 $$\upmu$$ μ g/m $$^{3}$$ 3 ) and the lowest in Santiago de Surco (SS) (PM $$_{10}$$ 10 -SS: 40.2 $$\upmu$$ μ g/m $$^{3}$$ 3 ). 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 $$_{10}$$ 10 values in SJM and warm months increased it, favored by thermal inversion (TI). Dust resuspension, vehicular transport and stationary sources contributed more PM $$_{10}$$ 10 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 $$_{10}$$ 10 (r = 0.5753); the RMSE-MLR (12.92) exceeded that found in the 3D models (RMSE $$ |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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