key: cord-0908566-aisccrha
authors: Sannigrahi, Srikanta; Pilla, Francesco; Maiti, Arabinda; Bar, Somnath; Bhatt, Sandeep; kaparwan, Ankit; Zhang, Qi; Keesstra, Saskia; Cerda, Artemi
title: Examining the status of forest fire emission in 2020 and its connection to COVID-19 incidents in West Coast regions of the United States
date: 2022-01-29
journal: Environ Res
DOI: 10.1016/j.envres.2022.112818
sha: 4289449a1e80bfbd9d4b7a078718ad626a23286e
doc_id: 908566
cord_uid: aisccrha

Forest fires impact on soil, water, and biota resources. The current forest fires in the West Coast of the United States (US) profoundly impacted the atmosphere and air quality across the ecosystems and have caused severe environmental and public health burdens. Forest fire led emissions could significantly exacerbate the air pollution level and, therefore, could play a critical role if the same occurs together with any epidemic and pandemic health crisis. Limited research is done so far to examine its impact in connection to the current pandemic. As of October 21, nearly 8.2 million acres of forest area were burned, with more than 25 casualties reported so far. In-situ air pollution data were utilized to examine the effects of the 2020 forest fire on atmosphere and coronavirus (COVID-19) casualties. The spatial-temporal concentrations of particulate matter (PM(2.5) and PM(10)) and Nitrogen Dioxide (NO(2)) were collected from August 1 to October 30 for 2020 (the fire year) and 2019 (the reference year). Both spatial (Multiscale Geographically Weighted Regression) and non-spatial (Negative Binomial Regression) analyses were performed to assess the adverse effects of fire emission on human health. The in-situ data-led measurements showed that the maximum increases in PM(2.5), PM(10), and NO(2) concentrations (μg/m(3)) were clustered in the West Coastal fire-prone states during August 1 – October 30, 2020. The average concentration (μg/m(3)) of particulate matter (PM(2.5) and PM(10)) and NO(2) was increased in all the fire states severely affected by forest fires. The average PM(2.5) concentrations (μg/m(3)) over the period were recorded as 7.9, 6.3, 5.5, and 5.2 for California, Colorado, Oregon, and Washington in 2019, increasing up to 24.9, 13.4, 25.0, and 17.0 in 2020. Both spatial and non-spatial regression models exhibited a statistically significant association between fire emission and COVID-19 incidents. Such association has been demonstrated robust and stable by a total of 30 models developed for analyzing the spatial non-stationary and local association. More in-depth research is needed to better understand the complex relationship between forest fire emission and human health.

Forest fire becomes a global environmental threat across ecosystems, causing severe 53 public health burdens via the upsurges of smokes and particulate matter concentration into More details about the COVID data collection process, quality assurance, data collection 173 assumptions, flag detection and reporting, etc., can be found on the USAFacts website. of spatial autocorrelation in parameter estimates. GWR is also sensitive to bandwidth and 211 kernel selection and parametrization, which seeks special attention while designing the spatial 212 models to explain any spatial varying relationship between parameters.

Recently, an extension to the existing GWR frameworks known as multiscale GWR

(MGWR) allows exploring the locally varying association between the parameters at a unique 215 spatial scale which eventually helps to understand the multiscale analysis of spatial also incorporated in the assessment to examine the impact of forest fire on air quality of the 279 regions and to statistically assess changes in air pollution concentration during the fire period.

The distribution and ranges of the air pollution concentration were measured using minimum, (Fig. 2) . A similar changing pattern is observed for PM10 and NO2 concentration during the 308 study period (Fig. 3 and Fig. 4) . (Table S1 ). However, for NO2, a decreasing monthly averaged concentration 334 (µg/m 3 ) was observed during the study period ( Fig. 6 and Table S1 ). Changes in air pollution 335 concentration due to forest fire are also examined and presented in Fig. 6, Fig. 7 States have shown the declining status of NO2 concentration in 2020 (Table S2) . This 342 happened due to the partial/fully lockdown imposed in many states due to the outbreak of 343 COVID in 2020. In addition to this, the State-wise monthly averaged concentration of PM2.5,

PM10, and NO2 is also measured and presented in Fig. 6 , Table S3, Table S4, Table S5 . 345 These tables and figures collectively suggest that PM2.5, PM10, and NO2 concentrations were Washington, no such close association between the fire emission and COVID counts have 376 observed (Fig. 8) . To further extend the correlation analysis, a correlation matrix has been 377 drawn consisting of month-wise distribution of air pollution estimates and COVID counts in 378 the fire States (Fig. 9, Fig. 10 ). Fig. 9 was illustrated using the COVID and air pollution States. COVID cases and death were statistically significantly correlated with PM2.5 October, 382 PM10 October, NO2 September and NO2 October estimates (Fig. 9) . However, considering 383 the average COVID-19 numbers and air pollution values of the four fire States, a moderate 384 association between the COVID counts and air pollution was found for all three months 385 considered in this study (Fig. 10, Table 2 ). 386 The outcomes of the spatial regression analysis between the averaged (average of 387 three months, i.e., August, September, October) air pollution values and COVID-19 counts 388 are presented in Fig. 11 and Fig. 12 Colorado and California counties. In contrast, lower spatial R 2 values are measured for the 391 counties in Washington and Oregon ( Fig. 11 and Table 3 ). This implies a spatially localized Table 4 ). In addition to the spatial regression, the negative 407 binomial regression is also performed to examine the effect of dispersion into the modelling 408 outcomes (Table S7 for COVID-19 case and Table S8 for COVID-19 death). A total of two 409 NB models were performed to examine the association between the explanatory and response Table. 1 Non-parametric test to evaluate mean differences in PM2.5, PM10, and NO2 concentration between fire (2020) and reference (2019) year. J o u r n a l P r e -p r o o f Table. 3 Spatial regression estimates derived from MGWR model. A total of 18 models were developed for both cases and death factors. 

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