Statistical modeling approach for PM10 prediction before and during confinement by COVID-19 in South Lima, Perú
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. Principa...
Guardado en:
| Autores principales: | , , , , , , , , , |
|---|---|
| Formato: | Artículo |
| Lenguaje: | inglés |
| Publicado: |
2022
|
| Materias: | |
| Acceso en línea: | Ver en el sistema de origen Descargar el archivo Enlace permanente (DOI o Handle) Enlace permanente (DOI o Handle) |
| Etiquetas: |
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1881508851513032704 |
|---|---|
| author | Cabello-Torres, Rita Jaqueline Ponce Estela, Manuel Angel Sánchez-Ccoyllo, Odón Romero-Cabello, Edison Alessandro García Ávila, Fausto Fernando Castañeda-Olivera, Carlos Alberto Valdiviezo-Gonzales, Lorgio Quispe Eulogio, Carlos Enrique Huaman de la Cruz, Alex Ruben López Gonzales, Javier Linkolk |
| author_browse | Cabello-Torres, Rita Jaqueline Castañeda-Olivera, Carlos Alberto García Ávila, Fausto Fernando Huaman de la Cruz, Alex Ruben López Gonzales, Javier Linkolk Ponce Estela, Manuel Angel Quispe Eulogio, Carlos Enrique Romero-Cabello, Edison Alessandro Sánchez-Ccoyllo, Odón Valdiviezo-Gonzales, Lorgio |
| author_facet | Cabello-Torres, Rita Jaqueline Ponce Estela, Manuel Angel Sánchez-Ccoyllo, Odón Romero-Cabello, Edison Alessandro García Ávila, Fausto Fernando Castañeda-Olivera, Carlos Alberto Valdiviezo-Gonzales, Lorgio Quispe Eulogio, Carlos Enrique Huaman de la Cruz, Alex Ruben López Gonzales, Javier Linkolk |
| author_sort | Cabello-Torres, Rita Jaqueline |
| collection | Producción científica |
| description | 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. |
| format | Article |
| id | oai:cris.upeu.edu.pe:20.500.15037/1333 |
| institution | UPeU |
| language | English |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| record_format | dspace |
| spelling | oai:cris.upeu.edu.pe:20.500.15037/13332026-10-30T16:47:44ZStatistical modeling approach for PM10 prediction before and during confinement by COVID-19 in South Lima, PerúCabello-Torres, Rita JaquelinePonce Estela, Manuel AngelSánchez-Ccoyllo, OdónRomero-Cabello, Edison AlessandroGarcía Ávila, Fausto FernandoCastañeda-Olivera, Carlos AlbertoValdiviezo-Gonzales, LorgioQuispe Eulogio, Carlos EnriqueHuaman de la Cruz, Alex RubenLópez Gonzales, Javier LinkolkEnvironmental sciencesEnvironmental social sciencesA 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.Articlehttps://pubmed.ncbi.nlm.nih.gov/36202880https://www.nature.com/articles/s41598-022-20904-2.pdfhttps://doi.org/10.1038/s41598-022-20904-2PMC953731810.1038/s41598-022-20904-2https://hdl.handle.net/20.500.15037/133336202880W4303195480enScientific Reports2045-23222022-10-062026-06-13T09:53:40Z2026-06-13T09:53:40Z |
| spellingShingle | Environmental sciences Environmental social sciences Cabello-Torres, Rita Jaqueline Ponce Estela, Manuel Angel Sánchez-Ccoyllo, Odón Romero-Cabello, Edison Alessandro García Ávila, Fausto Fernando Castañeda-Olivera, Carlos Alberto Valdiviezo-Gonzales, Lorgio Quispe Eulogio, Carlos Enrique Huaman de la Cruz, Alex Ruben López Gonzales, Javier Linkolk Statistical modeling approach for PM10 prediction before and during confinement by COVID-19 in South Lima, Perú |
| title | Statistical modeling approach for PM10 prediction before and during confinement by COVID-19 in South Lima, Perú |
| title_full | Statistical modeling approach for PM10 prediction before and during confinement by COVID-19 in South Lima, Perú |
| title_fullStr | Statistical modeling approach for PM10 prediction before and during confinement by COVID-19 in South Lima, Perú |
| title_full_unstemmed | Statistical modeling approach for PM10 prediction before and during confinement by COVID-19 in South Lima, Perú |
| title_short | Statistical modeling approach for PM10 prediction before and during confinement by COVID-19 in South Lima, Perú |
| title_sort | statistical modeling approach for pm10 prediction before and during confinement by covid 19 in south lima peru |
| topic | Environmental sciences Environmental social sciences |
| topic_facet | Environmental sciences Environmental social sciences |
| url | https://pubmed.ncbi.nlm.nih.gov/36202880 https://www.nature.com/articles/s41598-022-20904-2.pdf https://doi.org/10.1038/s41598-022-20904-2 https://hdl.handle.net/20.500.15037/1333 |
| work_keys_str_mv | AT cabellotorresritajaqueline statisticalmodelingapproachforpm10predictionbeforeandduringconfinementbycovid19insouthlimaperu AT ponceestelamanuelangel statisticalmodelingapproachforpm10predictionbeforeandduringconfinementbycovid19insouthlimaperu AT sanchezccoylloodon statisticalmodelingapproachforpm10predictionbeforeandduringconfinementbycovid19insouthlimaperu AT romerocabelloedisonalessandro statisticalmodelingapproachforpm10predictionbeforeandduringconfinementbycovid19insouthlimaperu AT garciaavilafaustofernando statisticalmodelingapproachforpm10predictionbeforeandduringconfinementbycovid19insouthlimaperu AT castanedaoliveracarlosalberto statisticalmodelingapproachforpm10predictionbeforeandduringconfinementbycovid19insouthlimaperu AT valdiviezogonzaleslorgio statisticalmodelingapproachforpm10predictionbeforeandduringconfinementbycovid19insouthlimaperu AT quispeeulogiocarlosenrique statisticalmodelingapproachforpm10predictionbeforeandduringconfinementbycovid19insouthlimaperu AT huamandelacruzalexruben statisticalmodelingapproachforpm10predictionbeforeandduringconfinementbycovid19insouthlimaperu AT lopezgonzalesjavierlinkolk statisticalmodelingapproachforpm10predictionbeforeandduringconfinementbycovid19insouthlimaperu |