Spatio-temporal analysis of PM2.5 and policies in Northwestern South America

Casallas, Alejandro; Castillo-Camacho, Maria Paula; Guevara-Luna, Marco Andrés; González, Yuri; Sanchez, Edwin Ricardo; Belalcázar, Luis Carlos

doi:10.1016/j.scitotenv.2022.158504

Cited by 22 publications

(19 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…OR5 ) reveal that during the full lockdown, the concentrations were higher than in the pre-lockdown period, especially in the Colombia-Venezuela border, and in the North of Colombia, which is explained by the low cloud fraction that allow the increase of radiation and also by the presence of wildfires in this region (e.g., Ballesteros-González et al 2020 , Mendez-Espinosa et al 2020 ), that develop due to favorable meteorology conditions. These wildfires are correlated with ozone formation since the areas with the highest pollutant emissions are highly correlated with the zones where wildfires are developing, as reported by González ( 2021 ) and demonstrated by Casallas et al ( 2022a ). However, a detailed quantification of the contributions of ozone due to wildfires is out of the scope of this paper.…”

Section: Resultssupporting

confidence: 53%

“…Data related to hotspots were also analyzed for each study period, downloaded from the Fire Information for Resource Management System—FIRMS (NASA, 2021 ), with a 1 km 2 resolution per pixel and a quality larger than 75%. Both data, from HYSPLIT and FIRMS, were analyzed together to find the importance of long-range transport (LRT) to increase pollutant concentrations (Casallas et al 2022a ). To have a more robust analysis, we follow the method of Casallas et al ( 2022a ) which is based on Mendez-Espinosa et al ( 2020 ) and consist of applying an algorithm that identifies the active wildfires that are causally related to the back trajectory and the air quality at the selected cities.…”

Section: Methodsmentioning

confidence: 99%

“…Both data, from HYSPLIT and FIRMS, were analyzed together to find the importance of long-range transport (LRT) to increase pollutant concentrations (Casallas et al 2022a ). To have a more robust analysis, we follow the method of Casallas et al ( 2022a ) which is based on Mendez-Espinosa et al ( 2020 ) and consist of applying an algorithm that identifies the active wildfires that are causally related to the back trajectory and the air quality at the selected cities. The algorithm creates a buffer zone of a 100 km radius around each of the 72 hourly locations that are defined for at least one of the trajectories reaching the study area.…”

Section: Methodsmentioning

confidence: 99%

“…Then, a Spearman correlation (Rho) is used to see the statistical correlation between air pollution and wildfires. To see a detailed explanation of this methodology, the reader is referred to Mendez-Espinosa et al ( 2020 ) and Casallas et al ( 2022a ).…”

Section: Methodsmentioning

confidence: 99%

“…These outliers are evaluated with the closest station to the station that produces the outlier at the same hour, to determine if the outlier is due to sensor error or due to a non-common phenomenon. Then the stations with more than 75% of representation between 2015 and 2019 and in 2020 are selected to be used in the analysis (Casallas et al 2022a ).…”

Section: Methodsmentioning

confidence: 99%

See 4 more Smart Citations

Surface, satellite ozone variations in Northern South America during low anthropogenic emission conditions: a machine learning approach

Casallas

Castillo-Camacho

Sanchez

et al. 2023

Air Qual Atmos Health

Self Cite

View full text Add to dashboard Cite

2020 presented the ideal conditions for studying the air quality response to several emission reductions due to the COVID-19 lockdowns. Numerous studies found that the tropospheric ozone increased even in lockdown conditions, but its reasons are not entirely understood. This research aims to better understand the ozone variations in Northern South America. Satellite and reanalysis data were used to analyze regional ozone variations. An analysis of two of the most polluted Colombian cities was performed by quantifying the changes of ozone and its precursors and by doing a machine learning decomposition to disentangle the contributions that precursors and meteorology made to form O 3 . The results indicated that regional ozone increased in most areas, especially where wildfires are present. Meteorology is associated with favorable conditions to promote wildfires in Colombia and Venezuela. Regarding the local analysis, the machine learning ensemble shows that the decreased titration process associated with the NO plummeting owing to mobility reduction is the main contributor to the O 3 increase (≈50%). These tools lead to conclude that (i) the increase in O 3 produced by the reduction of the titration process that would be associated with an improvement in mobile sources technology has to be considered in the new air quality policies, (ii) a boost in international cooperation is essential to control wildfires since an event that occurs in one country can affect others and (iii) a machine learning decomposition approach coupled with sensitivity experiments can help us explain and understand the physicochemical mechanism that drives ozone formation. Supplementary Information The online version contains supplementary material available at 10.1007/s11869-023-01303-6.

show abstract

Section: Resultssupporting

confidence: 53%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 3 more Smart Citations

Surface, satellite ozone variations in Northern South America during low anthropogenic emission conditions: a machine learning approach

Casallas

Castillo-Camacho

Sanchez

et al. 2023

Air Qual Atmos Health

Self Cite

View full text Add to dashboard Cite

show abstract

Design of an energy vulnerability index — spatial and temporal analysis: case of study Colombia

Guevara-Luna

Ramirez

Casallas

et al. 2022

Environ Sci Pollut Res

View full text Add to dashboard Cite

Intra-urban variability of long-term exposure to PM2.5 and NO2 in five cities in Colombia

Rodriguez-Villamizar,

Rojas,

Grisales

et al. 2023

Environ Sci Pollut Res

View full text Add to dashboard Cite

Rapidly urbanizing cities in Latin America experience high levels of air pollution which are known risk factors for population health. However, the estimates of long-term exposure to air pollution are scarce in the region. We developed intraurban land use regression (LUR) models to map long-term exposure to fine particulate matter (PM2.5) and nitrogen dioxide (NO2) in the five largest cities in Colombia. We conducted air pollution measurement campaigns using gravimetric PM2.5 and passive NO2 sensors for 2 weeks during both the dry and rainy seasons in 2021 in the cities of Barranquilla, Bucaramanga, Bogotá, Cali, and Medellín, and combined these data with geospatial and meteorological variables. Annual models were developed using multivariable spatial regression models. The city annual PM2.5 mean concentrations measured ranged between 12.32 and 15.99 µg/m3 while NO2 concentrations ranged between 24.92 and 49.15 µg/m3. The PM2.5 annual models explained 82% of the variance (R2) in Medellín, 77% in Bucaramanga, 73% in Barranquilla, 70% in Cali, and 44% in Bogotá. The NO2 models explained 65% of the variance in Bucaramanga, 57% in Medellín, 44% in Cali, 40% in Bogotá, and 30% in Barranquilla. Most of the predictor variables included in the models were a combination of specific land use characteristics and roadway variables. Cross-validation suggests that PM2.5 outperformed NO2 models. The developed models can be used as exposure estimate in epidemiological studies, as input in hybrid models to improve personal exposure assessment, and for policy evaluation.

show abstract

Spatio-temporal analysis of PM2.5 and policies in Northwestern South America

Cited by 22 publications

References 36 publications

Surface, satellite ozone variations in Northern South America during low anthropogenic emission conditions: a machine learning approach

Surface, satellite ozone variations in Northern South America during low anthropogenic emission conditions: a machine learning approach

Design of an energy vulnerability index — spatial and temporal analysis: case of study Colombia

Intra-urban variability of long-term exposure to PM2.5 and NO2 in five cities in Colombia

Contact Info

Product

Resources

About