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

doi:10.21203/rs.3.rs-3337744/v1

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Research Article

Spatialized PM _2.5 during Covid-19 Pandemic in Brazil’s most populous Southern City: Implications for Post-Pandemic Era

https://doi.org/10.21203/rs.3.rs-3337744/v1

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published 15 Jan, 2024

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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 (PM_2.5), 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 PM_2.5 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.

Air pollution

Particulate Matter

COVID-19

Low-cost sensors

Spatialization

Air pollution is recognized as one of the greatest environmental risks to public health (GBD 2019 Diseases and Injuries Collaborators, 2020). Epidemiological studies conducted over the past decade have consistently linked exposure to air pollution, especially PM_2.5 (fine particulate matter of aerodynamic size smaller than 2.5 µm), and a wide range of acute and chronic health effects, including respiratory and cardiovascular diseases (West et al., 2016; WHO, 2021a). Furthermore, evidence suggests a connection between air pollution and respiratory virus infection (Ciencewicki and Jaspers, 2007; Loaiza-Ceballos et al., 2022).

Studies indicate that high levels of PM_2.5 pollution can increase the risk of COVID-19 incidence, disease severity, and case fatality (Conticini et al., 2020; Travaglio et al. 2021; Veronesi et al., 2022; Wu et al., 2020; Zheng et al., 2020).

Studies conducted primarily in Europe, the United States, and China (Conticini et al., 2020; Travaglio et al., 2021; Veronesi et al., 2022; Wu et al., 2020; Zheng et al., 2020) have established associations specific levels of pollution with extensive geographic regions. However, it’s important to note the relationship between PM_2.5 and COVID-19 is not uniformly distributed and is influenced by social and economic determinants (Bossak and Andritsch, 2022; Ferreira et al.,2022).

Lower-income individuals spend more time in traffic or areas with higher airborne contaminants, increasing their exposure (da Motta Singer et al., 2023). Moreover, a higher prevalence of coexisting health conditions, nutritional deficiencies, limited access to information, and constrained education due to economic constraints contribute to an amplified susceptibility (da Motta Singer et al., 2023).

The COVID-19 pandemic has triggered an unprecedented global crisis, with an alarming increase in infections and mortality. In addition to the immediate impact of the virus, the emergence of post-COVID symptoms, including Long Covid, has become a major source of concern.

In Brazil, the impact of COVID-19 has been significant, with more than 37 million confirmed cases and more than 701,000 deaths as of April 2023, making it one of the countries most affected by the pandemic (WHO, 2023). Furthermore, almost 61,000 people die each year in Brazil due to PM air pollution (GBD 2019 Diseases and Injuries Collaborators, 2020). However, similar to many middle and low-income regions worldwide (Martin et al., 2019), Brazil faces challenges in terms of low air quality monitoring coverage and a high rate of discontinuity of the monitoring stations due to the high deployment, maintenance, and operation costs associated with these sites (IEMA, 2014).

Therefore, using low-cost sensors (LCS) has emerged as an alternative for enhancing the quantity and spatial coverage of air pollution monitoring data. The instruments also enable the collection of a large amount of data with high resolution, associated with low maintenance (Barkjohn et al., 2021).

This study investigated the association between PM_2.5 levels and COVID-19 cases and deaths in a densely populated city in Brazil, Curitiba. A low-cost sensor was employed to enhance the spatial representation of the data and identify priority regions for public health intervention. The analysis of COVID-19 data from 2020–2021 played a crucial role in understanding the patterns and impacts of the pandemic on various aspects of urban life. Furthermore, the findings of this study can contribute to the formulation of interventions that align with the needs and aspirations of urban residents, considering their socio-economic vulnerabilities.

2.1 Study area

A network of 9 low-cost sensors (LCS) was deployed in the city of Curitiba, the capital of the state of Paraná State in Brazil, and its metropolitan region in an industrial city of Araucaria (Fig. 1). With a population of around two million people, Curitiba ranks as an eight most populous city in Brazil (IBGE 2021). The city has a humid subtropical climate with an annual rainfall of 1631 mm and a historical average monthly temperature of 17.8°C, with a minimum of 9.3°C and maximum of 27,1°C. The city’s average altitude of 935 m above sea level makes it one of the coldest capitals in Brazil. During the winter season, the city is usually drier, with the lowest monthly average rainfall recorded in August at 81.5 mm (National Meteorological Institute – INMET, 2020). The preferential wind directions from the East and Northeast.

Araucaria, located 27 km from downtown Curitiba, is one of the most important agricultural and petrochemical industrial centers in the state. It had an estimated population of 148,55 inhabitants in 2021 (IBGE, 2021b), and its industrial park occupies approximately 10% of the city’s territory (Araucaria City Hall, 2021).

2.2 Study data

2.2.1. PM_2.5 data

PurpleAir LCS (PA-II SD or PA) was used to collect granular humidity and PM_2.5 mass concentration data from eight regions within Curitiba’s administrative Sanitary District (SD), as well as one point located in the industrial region of Araucaria, as illustrated in Fig. 1. The distribution of the sampling equipment considered that Curitiba is divided into ten sanitary districts (SD) for individual administration, which exhibit similar urban characteristics. The devices installed at points 1 and 9 are within the perimeter of the Monitoring Stations of the state environmental agency. Sampling point 3 was located at the Meteorological Station of INMET on the Campus of the Center for Technology of the Federal University of Paraná (UFPR). This location has been the subject of studies for approximately 5 years, providing historical data that contributed to a pre-assessment of pollution levels (Polezer et al., 2019). The remaining samplers were installed at the Parana Military Police Battalions (PM-PR) due to their central, secure, easily accessible, and internet-available locations. The central sampling point in Curitiba, known as SDMZ (Matriz Health District, marked as number 1 in Fig. 1), had the longest sampling duration, spanning from May 2020 to December 2021, while the other sites monitored the pollutants in 2021. Additionally, Fig. 1 depicts the main zoning areas of the city (IPPUC, 2021), which are delimited by colors.

Notably, during the study period, the official air quality monitoring network in the Curitiba region was limited to just three operational sites: one in Curitiba and two in Araucaria. However, it was only in 2021 that the environmental agency initiated the monitoring of PM_2.5, in only one of its points located in Araucaria.

PurpleAir sensors (PA-II-SDs) contain two Plantower PMS5003 (two channels, A and B) optical particle counters (Plantower Beijing Co. Ltd.) that use the principle of light scattering to convert scattered light into voltage using a photodiode detector. The correlation between the PM concentrations measured by channels A and B allows for assessing the performance and reliability of the PA-II sensors (Purpleair 2020).

These low-cost sensor (LCS) devices were positioned at an approximate height of 2 meters above ground level, in accordance with the air quality sampling guidelines established by the European Directive 2008/50/EC and the recommendations of the Environmental Protection Agency (EPA) (Clements et al., 2022; European Parliament, 2008).

Tests for measuring pollution data were conducted with the 9 sensors positioned adjacently under similar conditions as the study monitoring network to assess the compatibility of the data. Additionally, a separate test was conducted in a garage to assess consistency in measuring higher PM_2.5 concentrations. The data revealed a strong linear relationship between 5-minute mean PM_2.5 concentration generated by each channel (A versus B) of every PurpleAir LCS (Fig. S1a). Although approximately 40% of the measured concentration were found to be below the sensor accuracy threshold of 4.5 µg.m^-3 (Magi et al., 2020). These findings underscore the reliability of the LCS reported data with low intra-model variability, as the ratio (channel A/channel B) closely approximated the ideal values of 1 (Fig. S1b).

Therefore, the correspondence of PM_2.5 values generated by each sampler was evaluated. The correlation matrix of the PM_2.5 concentrations after the data processing procedure (Fig. S2) indicates a highly satisfactory comparison of the of the samplers, with R² > 0.95. After this step, the sensors were distributed to the sample points, allowing the comparison of their data. Further information on the tests and their results can be accessed in the Supplementary Materials.

2.2.2 Data treatment for PM_2.5 mass concentration

The measurement of PM by optical sensors, such as the PA, is subject to significant influence from local meteorological conditions, including temperature and relative humidity (RH), and the physical-chemical characteristics of the aerosols (Barkjohn et al., 2021).

A literature review on the subject indicates that studies worldwide have utilized the PA to establish correction equations to enhance the reliability of data. The South Coast Air Quality Management District (South Coast AQMD) has extensively evaluated the PA sensors. Their test has demonstrated strong agreement with two reference monitors for PM_2.5 (R² = 93% and 86%) (South Coast AQMD 2016). However, studies often focus on a limited geographic area, a specific season, or a narrow range of conditions (Ardon-Dryer et al., 2020; GBD 2019 Diseases and Injuries Collaborators, 2020; Kelly et al., 2017; Rose Eilenberg et al., 2020). Field tests using optical sensors have demonstrated the crucial role of relative humidity (RH) in PM measurements. Increased ambient humidity leads to the hygroscopic growth of the particulate matter, resulting in "fictitious" aerosol growth that generates a higher light scattering coefficient. Ultimately, this leads to an overestimation of the mass measured by the sensors (Barkjohn et al., 2021).

Daily averages of PM_2.5 mass concentration were corrected using a formula derived from an extensive research study conducted in partnership with the United States Environmental Protection Agency (USEPA) to improve the reliability of PA sensor data (Barkjohn et al., 2021). The correction model indicates that the use of RH reduced the bias and improved consistency. Given the broad regional and temporal scope, as well as the wide range of humidity levels evaluated in the study, this research utilizes the proposed linear model as per Eq. (1):

$${PM}_{2.5} =0.524 x PAc{f}_{1}– 0.0862 x RH +5.75$$

The correction factor (cf_1) for the PA sensor was computed as the average of the A and B channels, denoted by PAcf_1, and RH is expressed as a percentage. According to Barkjohn et al, (2021), the proposed correction equation, along with an additional data validation criterion, has a mean squared error of ± 2.5 µg.m^-3.

Additionally, a data validation procedure was conducted to ensure the consistency of data generated by the two Plantower sensors (channels A and B) within the PurpleAir sensor. Evidence suggests that discrepancies in the data may occur due to spiders, insects, or other minor obstruction, which have the potential to self-resolve (Barkjohn et al., 2021). The procedure was based on two metrics (i) the difference in data between the two particle counters integrated within the PA-II-SD (channels A and B) had to be less than 5 µg.m^-3 and (ii) the percent error of the typical 24-hour average agreement between the A and B channels (Eq. (2)) was rejected if it exceeded two standard derivations (2 SD = 61%) (Barkjohn et al., 2021)

$$24 percent difference = \frac{(A-B) }{(A+B)} X 2$$

2.2.3 Mass concentration accuracy validation.

Daily PM_2.5 mass sampling was conducted for a one-month period in December 2020 period using an inertial low-volume Harvard impactor (HI) sampler, which was co-located with a PA sensor at the SDMZ sampling site. Sampling was conducted as previously described in Polezer et al. (2019)

The detailed analysis of the test results presented in the supplementary material (Fig. S3 and S4) demonstrates a strong linear correlation (R² = 0.83) between the measurements obtained from the two instruments, following required data correction as mentioned in the previous section.

2.2.4 Health and mobility data

The health data on the number of COVID-19 cases and deaths in Curitiba and Araucaria, segmented by SD and locality, was collected from publicly available information provided by the Health Secretariats of Curitiba and Parana, with the assistance of the Modinterv portal (UFPR, 2021). Additionally, the number of transportation passengers was obtained from the local management company of Curitiba´s transportation system (URBS).

1.1 Data analysis

2.3.1 Spatialization of PM_2.5 data

Data from all monitoring stations were collected, and monthly averages of PM_2.5 concentration were calculated from March 2021 to December 2021, during which all stations were operational. These monthly mean values were then spatialized across Curitiba regions and the city of Araucaria using the interpolation technique of ordinary Kriging. The analysis was conducted using QGis v.3.22 geoprocessing software with support from "Processing R Provider" and its “zonal statistics” algorithm in a geographical coordinate system. The pixel resolution of the interpolation output was 90x90 meters.

The shape files with the district delimitations were obtained from the Institute for Research and Urban Planning Platform of Curitiba (IPPUC, 2021), while the data for Araucaria were directly provided by its City Hall.

2.3.2 Socio-economic index

When conducting spatialized evaluations of pollution's impact on health, it becomes essential to consider the socio-economic attributes unique to the local population. In this study, the Municipal Human Development Index (MHDI) of the respective municipality or region is integrated into the analysis for each health district in Curitiba and the city of Araucaria.

The data was obtained from the Atlas Brasil platform, developed in collaboration with the United Nations Development Programme, the Institute of Applied Economic Research, and the João Pinheiro Foundation, utilizing consolidated data from the Brazilian Institute of Geography and Statistics (IBGE) for the year 2010 (United Nations Development Programme et al., 2023).

It is important to emphasize that, due to the data being consolidated from 2010, the Tatuquara health district (SDTQ) was encompassed within the Pinheirinho health district (SDPN). For the purpose of comparison, the MHDI of SDTQ was treated as equivalent to SDPN.

Following the same criteria for evaluating human development as the HDI, specifically health (measured by life expectancy at birth), knowledge (educational access), and standard of living (per capita income), these localized MHDI indicators are tailored to align with regional contexts.

Through this process, "goalposts" are established as benchmarks for each dimension, positioning each location in accordance with its HDI value. This positioning is represented as a numerical value ranging between 0 and 1. The closer the value is to 1.000, the higher the level of human development for a federal unit, municipality, or region (United Nations Development Programme, 2023; United Nations Development Programme et al., 2023)

2.3.3 Health impacts

The present study employed a distributed lag model, a widely adopted method for analyzing time-series data (Atkinson et al., 2010; Li et al., 2020) to estimate the relationship between air pollution exposure and the risk of COVID-19 infection and mortality. A time interval (lag) was integrated to account for the delay between exposure and health outcomes. This integration identified an association between increases in air pollution exposure and the likelihood of adverse health outcomes. The model was implemented to evaluate the increase in percentage risk of a health outcome that could have impacts on the same day (lag 0), the following day (lag 1), or up to several days after exposure. The proposed model used a lag time of 0 to 28 days, based on the virus incubation time and disease-associated effects reported by the Brazilian Ministry of Health (Ministry of Health, 2022).

A potential weakness of this article is that it lacks a comparison group or control group for assessing the association between PM_2.5 exposure and COVID-19 cases and deaths. Without a control group, it is difficult to establish a causal relationship between air pollution and COVID-19 outcomes. However, it is important to highlight the potential impact of the study's findings in addressing the COVID-19 pandemic in Brazil, a country heavily affected by the virus. This emphasizes the need for targeted interventions and policies to reduce air pollution and its association with COVID-19 outcomes.

It is important to highlight the methodological advancements in this study. The researchers employed a novel approach by using low-cost sensors for air pollution monitoring and spatialization of data in the Brazilian region. This innovative method shows great potential for expanding air quality monitoring coverage, especially in resource-constrained regions. By utilizing low-cost sensors, it becomes possible to gather comprehensive data on air pollution, providing valuable insights into its health impacts and aiding in the development of targeted interventions and policies.

3.1. Spatial and temporal analysis of PM_2.5

The monthly average maps of PM_2.5 concentration for the period of March to December 2021 were generated using spatial interpolation and are shown in Fig. 2. PM_2.5 mass concentrations in Curitiba during the period of this study exceeded the annual average WHO guideline of 5 µg.m^-3 (WHO, 2021b) in all locations. These concentrations were similar to those reported in a previous study conducted in the city between 2008 and 2015, with an average value of 9.7 µg.m³ (Polezer et al. 2019), and within the observed average of 11.7 µg.m^-3 for urban cities in Brazil (Health Effects Institute -HEI, 2020). The maps illustrate the regional distribution of the pollutant, indicating a general gradual increase in pollutant concentration along an imaginary line from the north and northeast towards the south and industrial regions. The differences in PM_2.5 levels reflect the characteristics of each city region, as represented in Fig. 1. Therefore, the study identified three main clusters of regions based on the daily and annual levels of pollution, as well as the Pearson correlation generated using the daily mean concentrations of PM_2.5 for all monitored regions (detailed in the supplementary material, Fig. S5).

The first cluster consists of the industrial areas of SDTQ, SDCIC, and the city of Araucaria, which exhibit the highest concentrations of PM_2.5, with about 28% of the days exceeding the value recommended by WHO air quality guideline (15 µg.m^-3) (WHO, 2021b) and there is a high average Pearson correlation coefficient of 0.91 among these regions, Fig S5(b).

The second cluster includes the residential districts of SDSF and SDCJ, which exhibit lower pollution concentrations and demonstrate a high correlation with each other (r = 0.90).

The third cluster comprises the intermediated regions of SDBV, SDPR, SDBQ, along with Curitiba Downtown (SDMZ) which are highly correlated with each other (ranging between 0.81 and 0.94) and have lower correlations with the residential sites. These urban regions are categorized as intermediate regions in terms of their PM_2.5 concentrations as showed in Fig. 1, positioned between the highly polluted industrial sites and the less polluted residential regions, Consequently, these regions are influenced by traffic flow from nearby highways, high-flow lanes with commercial establishments, and proximity to industries. Moreover, the cities situated north of Curitiba have the highest concentrations of limestone mines in the state of Parana, which could result in a significant environmental impact due to the emission of particulate matter from these activities, s as observed in the SDBV region. Despite being predominantly residential (Fig. 1), this district demonstrated higher concentrations of particulate matter (Fig. S5 (a)) compared to the regions in the second cluster and exhibited intermediate correlations when compared to the other regions (Fig. S5 (b)).

The maps also indicate that the months with the highest PM_2.5 concentration are from June to September, whereas October, November, and December exhibited the lowest concentrations. Increased pollutant levels coincide with periods of reduced rainfall (INMET, 2021), due to the “washout“ effect" (Seinfeld et al., 2016). Furthermore, lower temperatures during winter days and the absence of rain (Fig. S6) exacerbate the occurrence of thermal inversion, leading to the accumulation of atmospheric pollutants in the lower atmospheric layers.

The seasonal variation of PM_2.5, with higher concentrations in winter months compared to summer, has been observed in other locations worldwide, including Beijing in China (Liu et al., 2017) and urban cities in Europe, such as Vienna, Amsterdam, and Stockholm (Adães and Pires, 2019). Furthermore, other studies conducted in Curitiba have also observed seasonal variation, with lower values reported by de Miranda et al. (2012) in summer (Dec-Feb) ranging from 8 µg.m^-3 to higher values of 16,7 µg.m^-3 in winter months (Jun-Aug). Polezer et al. (2018) also identified in Curitiba that the summer months presented a lower number of days with values above the 10 µg.m^-3.

3.2 Spatial Assessment of COVID-19 Health Impacts, socioeconomics impacts, and PM_2.5 Mass Concentration

The spatial analysis of health impacts reveals regional variations in mortality rates and incidence of COVID-19 cases (Fig. S7) during the two-year period. Furthermore, the majority of the regions examined exhibited annual rates (per 100,000 inhabitants) exceeding the Brazilian average for both years. In the case of Curitiba as a whole, the mortality rate in 2020 exceeded this average by approximately 35%, while the incidence rate was 59% higher. The city of Araucaria (9) is an exception, with a 6% lower mortality rate in 2020 compared to the national average in Brazil. However, it recorded the highest number of cases per 100,000 inhabitants during the two-year period, with 21,934 cases, surpassing the Brazilian rate by more than 96% in 2020 and accumulating a 33% higher rate in 2021.

Among the study regions, the city of Araucaria showcased the lowest MHDI (Fig. 3), ranking it 54th in the state of Paraná (IBGE, 2021). Furthermore, owing to its industrial nature, the city exhibited the highest average concentration of PM_2.5 in the monitoring conducted in 2021 as part of this study.

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).

The region of SDPN, situated in the intermediate regions of Curitiba, recorded the highest mortality rate per 100,000 inhabitants during the analyzed two-year period, with 499 deaths. Moreover, this region this region is also distinguished by being the second lowest index in Curitiba. These socioeconomic conditions may contribute to increased vulnerability among the population regarding COVID-19 transmission and may also impede access to medical services for diagnosis and treatment, as suggested in previous studies (Bermudi et al., 2021).

Furthermore, the central regions of the map, comprising SDPN, downtown SDMZ, and SDPR, exhibited significantly higher morbidity and mortality rates compared to the other regions. These areas are renowned for their significant influx of people, which serves as a contributing factor to the transmission of COVID-19 (WHO, 2023). For instance, SDPN host the two largest bus terminals in Curitiba, capable of accommodating over 252,000 people daily (IPPUC, 2021). Likewise, SDPR and SDMZ boast a significant concentration of commercial and service establishments, leading to an intensified influx of people in the respective regions.

In contrast, the northern/north-eastern regions (SDBV and SDSF) of Curitiba municipality displayed comparatively lower mortality and incidence rates of COVID-19. Notably, these areas corresponded to regions with a reduced spatial concentration of PM_2.5 and are predominantly residential in nature. Moreover, these regions possess higher MDHIs in comparison to the Southern/Southwestern regions depicted on the map.

Similar findings linking socioeconomic and geographic indicators, as well as air pollution, to COVID-19 case fatality rates and incidence have been reported in studies conducted in various countries, including the United States (Liang et al., 2020), Brazil (Lorenz et al., 2021), England (Travaglio et al., 2021), and China (Zheng et al., 2021).

In the statistical analysis conducted using the Pearson correlation through a distributed lag model over a time lag ranging from 0 to 28 days, most of the studied regions showed positive and significant Pearson correlations (Fig. S8) between PM_2.5 concentration and COVID-19 cases. The influence was particularly pronounced a lag period of 1 to 6 days (p-value > 0.01). Moreover, a significant positive correlation (p-value < 0.05) was identified between COVID-19 deaths and the spatialized concentration of PM_2.5, but this association was found only in the most polluted regions of the study.

These findings are in line with previous research that demonstrates the correlation between exposure to air pollution and the exacerbation of infectious diseases (Ciencewicki and Jaspers, 2007). Moreover, they are consistent with recent systematic reviews that have reported a positive correlation between PM_2.5 and both COVID-19 cases and deaths (Bhaskar et al., 2020; Copat et al., 2020).

3.3 Time Evolution of COVID-19 in Curitiba

The data presented in Fig. 4 provides a comprehensive evaluation of atmospheric pollution due to PM_2.5 and other significant factors related to the phases of COVID-19 and urban mobility in Curitiba. Notably, the SDMZ location, situated in the city center, exhibited a strong correlation of PM_2.5 mass concentration and all other measurement points (0.76 < r < 0.93), suggesting its characterization as a typical pollution hotspot in the city (Fig. S5b). Moreover, the data collection period in this area was the longest (2020–2021), further enhancing the significance of the findings.

The number of passengers using public transportation has frequently been used as an indicator of urban mobility during the pandemic period (Marra et al., 2022). However, it has been noticed that there is no direct relationship between the number of passengers and the levels of pollutants in the city. This is because PM_2.5 emission sources in the city are not exclusive to public transport. A study conducted in the city of Curitiba also found that, in addition to public transport, emission sources from private vehicles (cars) and subsequently from the local industries all contribute to the levels of particulate matter in the city (Gidhagen et al., 2021).

A comparative analysis was conducted on the periods before (May 2020 – April 2021) and after vaccination (May 2021) phases was performed to investigate their potential effects on the environment. The evaluation revealed a gradual increase in pollution compared to the corresponding months in previous years, coinciding with an increase in passenger traffic. These findings suggest that the gradual easing of mobility restrictions for the population and service operations following increased inoculation has contributed to higher pollution levels in the city. Thus, the observed deterioration in air quality can be attributed to these relaxations.

To examine this trend, we computed the Pearson correlation coefficient using a lag method, considering the concentration of PM_2.5 at the central location (SDMZ) as a representative measure of Curitiba. We analyzed four distinct periods - scenario: (1) Reference data from 2020 - "Curitiba 2020"; (2) Reference data from 2021 - "Curitiba 2021"; (3) Curitiba data from June to October 2020 - "Curitiba Jun/Oct 2020"; and (4) Curitiba data from July to October 2021 - "Curitiba Jul/Oct 2021." The comprehensive results, including p-values, can be found in Supplementary Materials, Fig. S9.

The highest significant coefficients were found for scenario 4, with mean values of 0.29 for the number of deaths and 0.28 for the number of cases. Furthermore, the findings demonstrated that shortening the evaluation period to the most polluted months of PM_2.5 exposure - "Curitiba Jun/Oct 2020" and "Curitiba Jul/Oct 2021" - exerted a more substantial impact on the health of the COVID-19-affected population compared to the broader annual correlations - "Curitiba 2020" and "Curitiba 2021" - which yielded negative mean r results (p-value < 0.05).

The data suggests that utilizing low-cost PM_2.5 sensor data is a feasible tool for conducting epidemiological research, especially because it enables improved spatial analysis in a cost-effective manner. Consequently, the findings of this study highlight the importance of incorporating air pollution as a component of an integrated approach to sustainable development, encompassing environmental and health preservation, to mitigate the impacts and repercussions of epidemics.

Through the examination of the correlation between air pollution and COVID-19, researchers can enhance their comprehension of how environmental factors impact public health. Within this exploration, a significant theme has emerged: environmental justice.

Environmental justice arises, as the impact of COVID-19 and particulate matter pollution is unequally distributed among communities. Disadvantaged communities and minority groups frequently bear a disproportionate burden.

The spatial analysis in this study is vital for identifying priority regions where municipalities should take action in a post-COVID era. Through identifying areas with high pollution levels and their connection to COVID-19 mortality and incidence, decision-makers can allocate resources effectively and implement focused measures. This proactive endeavor aims to foster a healthier and more resilient society for the future by enhancing public health measures and mitigating potential disparities.

Furthermore, this research emphasizes the potential frequency of pandemics, and its significant value lies in enhancing global preparedness for future respiratory viral outbreaks.

This study was supported in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001 and the Paraná Federal University (UFPR) for providing the equipment. The authors gratefully acknowledge the Paraná Military Police and the Instituto Água e Terra (IAT) - Paraná for providing safe locations for the installation of the equipment used in this research.

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You are reading this latest preprint version

Spatialized PM _2.5 during Covid-19 Pandemic in Brazil’s most populous Southern City: Implications for Post-Pandemic Era

Status:

Journal Publication

Version 1

Abstract

Figures

1. Introduction

2. Materials and Methods

2.1 Study area

2.2 Study data

2.2.1. PM_2.5 data

2.2.2 Data treatment for PM_2.5 mass concentration

2.2.3 Mass concentration accuracy validation.

2.2.4 Health and mobility data

2.3.1 Spatialization of PM_2.5 data

2.3.2 Socio-economic index

2.3.3 Health impacts

3. Results and discussion

3.1. Spatial and temporal analysis of PM_2.5

3.2 Spatial Assessment of COVID-19 Health Impacts, socioeconomics impacts, and PM_2.5 Mass Concentration

3.3 Time Evolution of COVID-19 in Curitiba

3. Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1

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

Status:

Journal Publication

Version 1

Abstract

Figures

1. Introduction

2. Materials and Methods

2.1 Study area

2.2 Study data

2.2.1. PM2.5 data

2.2.2 Data treatment for PM2.5 mass concentration

2.2.3 Mass concentration accuracy validation.

2.2.4 Health and mobility data

2.3.1 Spatialization of PM2.5 data

2.3.2 Socio-economic index

2.3.3 Health impacts

3. Results and discussion

3.1. Spatial and temporal analysis of PM2.5

3.2 Spatial Assessment of COVID-19 Health Impacts, socioeconomics impacts, and PM2.5 Mass Concentration

3.3 Time Evolution of COVID-19 in Curitiba

3. Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1

Spatialized PM _2.5 during Covid-19 Pandemic in Brazil’s most populous Southern City: Implications for Post-Pandemic Era

2.2.1. PM_2.5 data

2.2.2 Data treatment for PM_2.5 mass concentration

2.3.1 Spatialization of PM_2.5 data

3.1. Spatial and temporal analysis of PM_2.5

3.2 Spatial Assessment of COVID-19 Health Impacts, socioeconomics impacts, and PM_2.5 Mass Concentration