Assessing socioeconomic bias of exposure to urban air pollution: an autopsy-based study in São Paulo, Brazil

Singer, Júlio da Motta; André, Carmen Diva Saldiva de; André, Paulo Afonso de; Rocha, Francisco Marcelo Monteiro da; Waked, Dunia; Vaz, Aline Macedo; Gois, Gustavo Ferreira; Andrade, María de Fátima; Veras, Mariana Matera; Saldiva, Paulo Hilário Nascimento; Barrozo, Lígia Vizeu

doi:10.1016/j.lana.2023.100500

Cited by 6 publications

(5 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In general, regions that attained higher averages of particulate matter corresponded to areas with lower MHDI values (Fig. 3), aligning with previous studies (Clougherty et al, 2022;da Motta Singer et al, 2023).…”

Section: Spatial and Temporal Analysis Of Pm 25supporting

confidence: 91%

Spatialized PM 2.5 during Covid-19 Pandemic in Brazil’s most populous Southern City: Implications for Post-Pandemic Era

Costa,

Pauliquevis,

Heise

et al. 2023

Preprint

View full text Add to dashboard Cite

Brazil has experienced one of the highest COVID-19 fatality rates globally. While numerous studies have explored the potential connection between air pollution, specifically fine particulate matter (PM2.5), and the exacerbation of SARS-CoV-2 infection, the majority of this research has been conducted in foreign regions—Europe, the United States, and China—correlating generalized pollution levels with health-related scopes. In this study, we aim to delve into the localized relationship between air pollution exposure and its health implications within a specific Brazilian municipality, focusing on COVID-19 susceptibility. Our investigation involves assessing pollution levels through spatial interpolation of in-situ PM2.5 measurements. A network of affordable sensors collected data across 9 regions in Curitiba, as well as its metropolitan counterpart, Araucaria. Our findings distinctly unveil a significant positive correlation (r up to 0.36, p-value < 0.01) between more polluted areas, even more pronounced during winter months (r up to 0.40, p-value < 0.05), with both COVID-19 mortality and incidence rates. This correlation gains added significance due to the intricate interplay between urban atmospheric pollution and regional human development indexs. Notably, heightened pollution aligns with industrial hubs and intensified vehicular activity. The spatial analysis performed in this study assumes a pivotal role by identifying priority regions that require targeted action post-COVID. By comprehending the localized dynamics between air pollution and its health repercussions, tailored strategies can be implemented to alleviate these effects and ensure the well-being of the public.

show abstract

Section: Spatial and Temporal Analysis Of Pm 25supporting

confidence: 91%

Spatialized PM 2.5 during Covid-19 Pandemic in Brazil’s most populous Southern City: Implications for Post-Pandemic Era

Costa,

Pauliquevis,

Heise

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Pulsed radiation is generated by the corresponding radiation source (matrix of LEDs) (2), controlled by the control unit (4), and enters the chamber (1). The radiation passes through the measuring chamber (1), where part of the radiation energy is absorbed by the gas components, causing the formation of acoustic waves. These waves are detected by the sensor matrix (3), which consists of a set of spectral sensors and temperature and pressure sensors.…”

Section: Fig 2 Schematic Diagram Of the Gas Analyzermentioning

confidence: 99%

“…To constrain the search space during training, the task is to minimize the NS target error function, which is found using the least squares method [73] as shown in (1).…”

Section: Neural Network Modelmentioning

confidence: 99%

See 1 more Smart Citation

Intelligent Hardware-Software Processing of High-Frequency Scanning Data

Mukanova,

Atanov,

Jamshidi

2023

Journal of Robotics and Control

View full text Add to dashboard Cite

The constant emission of polluting gases is causing an urgent need for timely detection of harmful gas mixtures in the atmosphere. A method and algorithm of the determining spectral composition of gas with a gas analyzer using an artificial neural network (ANN) were suggested in the article. A small closed gas dynamic system was designed and used as an experimental bench for collecting and quantifying gas concentrations for testing the proposed method. This device was based on AS7265x and BMP180 sensors connected in parallel to a 3.3 V compatible Arduino Uno board via QWIIC. Experimental tests were conducted with air from the laboratory room, carbon dioxide (CO2), and a mixture of pure oxygen (O2) with nitrogen (N2) in a 9:1 ratio. Three ANNs with one input, one hidden and one output layer were built. The ANN had 5, 10, and 20 hidden neurons, respectively. The dataset was divided into three parts: 70% for training, 15% for validation, and 15% for testing. The mean square error (MSE) error and regression were analyzed during training. Training, testing, and validation error analysis were performed to find the optimal iteration, and the MSE versus training iteration was plotted. The best indicators of training and construction were shown by the ANN with 5 (five) hidden layers, and 16 iterations are enough to train, test and verify this neural network. To test the obtained neural network, the program code was written in the MATLAB. The proposed scheme of the gas analyzer is operable and has a high accuracy of gas detection with a given error of 3%. The results of the study can be used in the development of an industrial gas analyzer for the detection of harmful gas mixtures.

show abstract

“…Consequently, to achieve a cumulative dose of 600 μg/m3 (equivalent to the daily exposure levels experienced by individuals living in highly polluted cities like São Paulo and many others), the duration of exposure varied based on the daily ambient PM2.5 levels. The formula used to determine the exposure time in the HAPC was as follows: Time=600/(ambient PM2.5 concentration * 20) In the control group, animals were exposed to filtered air (FA) under identical conditions. A separate chamber equipped with high-efficiency particulate air (HEPA) filters was used to effectively remove PM2.5 from the air.…”

Section: Air Pollution Exposure Modelmentioning

confidence: 99%

Air Pollution Aggravates Renal Ischemia-Reperfusion-Induced Acute Kidney Injury

Sanches,

Parra,

Sun

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

Chronic kidney disease (CKD) has emerged as a significant global public health concern. Recent epidemiological studies have highlighted the link between exposure to fine particulate matter (PM2.5) and declined renal function. PM2.5 exerts its harmful effects on various organs through oxidative stress and inflammation. Acute kidney injury (AKI) resulting from ischemia reperfusion injury (IRI) involves similar biological processes involved in PM2.5 toxicity and is a known risk factor for CKD. The objective of this study was to investigate the impact of PM2.5 exposure on IRI-induced AKI. Mice were exposed to PM2.5 or filtered air for 12 weeks before IRI, and were euthanized 48h after IRI. Animals exposed to PM2.5 and IRI exhibited reduced glomerular filtration and impaired urine concentration ability. Moreover, they showed elevated tubular damage markers NGAL and KIM-1, along with significant tubular necrosis. PM2.5 exposure exacerbated local innate immune activation, leading to an increased infiltration of Ly6G+ granulocytes and F480+ macrophages in the kidney. This, in turn, contributed to heightened renal senescence markers and myofibroblast infiltration. Collectively, our findings suggest that AKI-induced hampered tubular function is worsened by PM2.5, leading to reduced resilience to stress, activation of aging mechanisms and early hallmarks of fibrosis. Decreasing PM2.5 and implementing preventive strategies can improve AKI patients outcome and prevent AKI progression.

show abstract

Assessing socioeconomic bias of exposure to urban air pollution: an autopsy-based study in São Paulo, Brazil

Cited by 6 publications

References 34 publications

Spatialized PM 2.5 during Covid-19 Pandemic in Brazil’s most populous Southern City: Implications for Post-Pandemic Era

Spatialized PM 2.5 during Covid-19 Pandemic in Brazil’s most populous Southern City: Implications for Post-Pandemic Era

Intelligent Hardware-Software Processing of High-Frequency Scanning Data

Air Pollution Aggravates Renal Ischemia-Reperfusion-Induced Acute Kidney Injury

Contact Info

Product

Resources

About