The Program to Improve the Air Quality of Mexicali, Baja California, Mexico 2010-2015

Quintero, Margarito; Meza, Lourdes; Canales, Miguel; Ahumada, Silvia

doi:10.1016/j.proenv.2010.10.091

Cited by 3 publications

(4 citation statements)

References 3 publications

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“…Nevertheless, the problem which historically characterized the area on both sides of the border is high levels of air pollution. According to specialists, Mexicali is one of the most air-polluted cities in the country and even in the northern hemisphere (Quintero et al, 2010;Martinez, 2019). Among others, air pollution is caused by multiple factors such as car smog, factory emissions, sand, and dust due to the composition and nature of the soil in Mexicali and Imperial County (Quintero & Tovar, 2019, p. 251).…”

Section: Dossier América Latina: Dimensiones Y Retos De Seguridadmentioning

confidence: 99%

Comparative Study of Deaths Related to COVID-19, Homicides, and Air Pollution in the Mexicali-Imperial County Cross-Border Region (2018–2020)

Reynoso¹

2023

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This comparative study analyzes the absolute numbers and death rates of air pollution-related fatal victims, intentional homicides, and COVID-19 losses from 2018 to 2020 in the Mexicali (Baja California, Mexico) and Imperial County (California, United States) cross-border region. The analysis is nourished with quantitative data from secondary sources, systematized by all three levels of government authorities in the case of Mexicali and federal authorities in the Imperial County case. Evidence reveals that air pollution, homicides, and COVID-19 have had a differential impact on both sides of the border. While the COVID-19 pandemic was devastating in multiple aspects, air pollution-related mortality rates in the Mexicali-Imperial County cross-border region were five times higher than those of COVID-19. Mexicali was the most distressed “side” of the border in absolute numbers. However, by population rate, Imperial County was the most disturbed. Therefore, this situation reflects a shared priority for state and municipal authorities on both sides of the border.

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Section: Dossier América Latina: Dimensiones Y Retos De Seguridadmentioning

confidence: 99%

Comparative Study of Deaths Related to COVID-19, Homicides, and Air Pollution in the Mexicali-Imperial County Cross-Border Region (2018–2020)

Reynoso¹

2023

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“…In this study, a lasso regularization technique is used with L 1 regularization presented in Equation (2). We can write the penalized versions to maximize it as follows:…”

Section: Logistic Regressionmentioning

confidence: 99%

“…Similar to any other developing metropolis, PdN is confronted with the ever-increasing issues of poor air quality. Additionally, as an international cross-border location, there is growing concern about its air quality for both the United States and Mexico [1,2].…”

Section: Introductionmentioning

confidence: 99%

Implementing Machine Learning Algorithms to Predict Particulate Matter (PM2.5): A Case Study in the Paso del Norte Region

Mahmud¹,

Ridi

Miah

et al. 2022

Atmosphere

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This work focuses on the prediction of an air pollutant called particulate matter (PM2.5) across the Paso Del Norte region. Outdoor air pollution causes millions of premature deaths every year, mostly due to anthropogenic fine PM2.5. In addition, the prediction of ground-level PM2.5 is challenging, as it behaves randomly over time and does not follow the interannual variability. To maintain a healthy environment, it is essential to predict the PM2.5 value with great accuracy. We used different supervised machine learning algorithms based on regression and classification to accurately predict the daily PM2.5 values. In this study, several meteorological and atmospheric variables were retrieved from the Texas Commission of Environmental Quality’s monitoring stations corresponding to 2014–2019. These variables were analyzed by six different machine learning algorithms with various evaluation metrics. The results demonstrate that ML models effectively detect the effect of other variables on PM2.5 and can predict the data accurately, identifying potentially risky territory. With an accuracy of 92%, random forest performs the best out of all machine learning models.

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“…Pollution in the Mexicali Valley (Mexico) is not a simple phenomenon. It involves factors that play essential roles in the region, both in the economic development and the environment, such as landfills, cement and smelting industries, agricultural burning, and power plants nearby [1][2][3][4]. The Cerro Prieto geothermal power plant, which is the primary source of energy in the state of Baja California (BC, Mexico), is frequently considered as the principal source of pollution in the region.…”

Section: Introductionmentioning

confidence: 99%

Estimations of the Mexicali Valley (Mexico) Mixing Height

et al. 2020

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We report estimations of the Mexicali Valley (Mexico) mixing height for three seasons. Surface and upper air meteorological measurements were carried out nearby Cerro Prieto geothermal power plant during July 2010 (summer), January 2012 (winter), and October 2016 (autumn). Four different methods were applied to estimate the convective boundary layer (CBL) height from radiosonde (RS) profiles: the parcel method, the gradients method, the least-squares variational approach based on the slab model of the CBL structure, and a covariance method. For nocturnal conditions, we used diagnostic models based on friction velocity and Monin–Obukhov length. Under unstable conditions, we obtained (on average) mixing heights of 497 m at 06:00 LST, 1242 m at 12:00 LST, 1404 m at 15:00 LST, and 482 at 18:00 LST during summer; 754 m at 12:00 LST during winter; and 1195 m at 12:00 LST and 13:00 m between the 14:00 and 15:00 LST during the autumn. The results allowed adjusting a semiempirical model to evaluate mixing height from turbulent sensible heat flux and friction velocity data. Our results provide practical tools that could facilitate the application of regulatory dispersion models to assess air quality in the region.

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The Program to Improve the Air Quality of Mexicali, Baja California, Mexico 2010-2015

Cited by 3 publications

References 3 publications

Comparative Study of Deaths Related to COVID-19, Homicides, and Air Pollution in the Mexicali-Imperial County Cross-Border Region (2018–2020)

Comparative Study of Deaths Related to COVID-19, Homicides, and Air Pollution in the Mexicali-Imperial County Cross-Border Region (2018–2020)

Implementing Machine Learning Algorithms to Predict Particulate Matter (PM2.5): A Case Study in the Paso del Norte Region

Estimations of the Mexicali Valley (Mexico) Mixing Height

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