Impact of Regional Mobility on Air Quality during COVID-19 Lockdown in Mississippi, USA Using Machine Learning.

Social distancing measures and shelter-in-place orders to limit mobility and transportation were among the strategic measures taken to control the rapid spreading of COVID-19. In major metropolitan areas, there was an estimated decrease of 50 to 90 percent in transit use. The secondary effect of the COVID-19 lockdown was expected to improve air quality, leading to a decrease in respiratory diseases. The present study examines the impact of mobility on air quality during the COVID-19 lockdown in the state of Mississippi (MS), USA. The study region is selected because of its non-metropolitan and non-industrial settings. Concentrations of air pollutants-particulate matter 2.5 (PM2.5), particulate matter 10 (PM10), ozone (O3), nitrogen oxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO)-were collected from the Environmental Protection Agency, USA from 2011 to 2020. Because of limitations in the data availability, the air quality data of Jackson, MS were assumed to be representative of the entire region of the state. Weather data (temperature, humidity, pressure, precipitation, wind speed, and wind direction) were collected from the National Oceanic and Atmospheric Administration, USA. Traffic-related data (transit) were taken from Google for the year 2020. The statistical and machine learning tools of R Studio were used on the data to study the changes in air quality, if any, during the lockdown period. Weather-normalized machine learning modeling simulating business-as-scenario (BAU) predicted a significant difference in the means of the observed and predicted values for NO2, O3, and CO (p < 0.05). Due to the lockdown, the mean concentrations decreased for NO2 and CO by -4.1 ppb and -0.088 ppm, respectively, while it increased for O3 by 0.002 ppm. The observed and predicted air quality results agree with the observed decrease in transit by -50.5% as a percentage change of the baseline, and the observed decrease in the prevalence rate of asthma in MS during the lockdown. This study demonstrates the validity and use of simple, easy, and versatile analytical tools to assist policymakers with estimating changes in air quality in situations of a pandemic or natural hazards, and to take measures for mitigating if the deterioration of air quality is detected.

Application of Machine Learning to Study the Association between Environmental Factors and COVID-19 Cases in Mississippi, USA.

Because of the large-scale impact of COVID-19 on human health, several investigations are being conducted to understand the underlying mechanisms affecting the spread and transmission of the disease. The present study aimed to assess the effects of selected environmental factors such as temperature, humidity, dew point, wind speed, pressure, and precipitation on the daily increase in COVID-19 cases in Mississippi, USA, during the period from January 2020 to August 2021. A machine learning model was used to predict COVID-19 cases and implement preventive measures if necessary. A statistical analysis using Python programming showed that the humidity ranged from 56% to 78%, and COVID-19 cases increased from 634 to 3546. Negative correlations were found between temperature and COVID-19 incidence rate (-0.22) and between humidity and COVID-19 incidence rate (-0.15). The linear regression model showed the model linear coefficients to be 0.92 and -1.29, respectively, with the intercept being 55.64. For the test dataset, the R2 score was 0.053. The statistical analysis and machine learning show that there is no linear dependence of temperature and humidity with the COVID-19 incidence rate.

The status of geo-environmental health in Mississippi: Application of spatiotemporal statistics to improve health and air quality.

Data enabled research with a spatial perspective may help to combat human diseases in an informed and cost-effective manner. Understanding the changing patterns of environmental degradation is essential to help in determining the health outcomes such as asthma of a community. In this research, Mississippi asthma-related prevalence data for 2003-2011 were analyzed using spatial statistical techniques in Geographic Information Systems. Geocoding by ZIP code, choropleth mapping, and hotspot analysis techniques were applied to map the spatial data. Disease rates were calculated for every ZIP code region from 2009 to 2011. The highest rates (4-5.5%) were found in Prairie in Monroe County for three consecutive years. Statistically significant hotspots were observed in urban regions of Jackson and Gulf port with steady increase near urban Jackson and the area between Jackson and meridian metropolis. For 2009-2011, spatial signatures of urban risk factors were found in dense population areas, which was confirmed from regression analysis of asthma patients with population data (linear increase of R2 = 0.648, as it reaches a population size of 3,5000 per ZIP code and the relationship decreased to 59% as the population size increased above 3,5000 to a maximum of 4,7000 per ZIP code). The observed correlation coefficient (r) between monthly mean O3 and asthma prevalence was moderately positive during 2009-2011 (r = 0.57). The regression model also indicated that 2011 annual PM2.5 has a statistically significant influence on the aggravation of the asthma cases (adjusted R-squared 0.93) and the 2011 PM2.5 depended on asthma per capita and poverty rate as well. The present study indicates that Jackson urban area and coastal Mississippi are to be observed for disease prevalence in future. The current results and GIS disease maps may be used by federal and state health authorities to identify at-risk populations and health advisory.

Spatio-Temporal Variation of Particulate Matter(PM2.5) Concentrations and Its Health Impacts in a Mega City, Delhi in India.

Rising concentration of air pollution and its associated health effects is rapidly increasing in India, and Delhi, being the capital city, has drawn our attention in recent years. This study was designed to analyze the spatial and temporal variations of particulate matter (PM2.5) concentrations in a mega city, Delhi. The daily PM2.5 concentrations monitored by the Central Pollution Control Board (CPCB), New Delhi during November 2016 to October 2017 in different locations distributed in the region of the study were used for the analysis. The descriptive statistics indicate that the spatial mean of monthly average PM2.5 concentrations ranged from 45.92 µg m-3 to 278.77 µg m-3. The maximum and minimum spatial variance observed in the months of March and September, respectively. The study also analyzed the PM2.5 air quality index (PM2.5-Air Quality Index (AQI)) for assessing the health impacts in the study area. The AQI value was determined according to the U.S. Environmental Protection Agency (EPA) system. The result suggests that most of the area had the moderate to very unhealthy category of PM2.5-AQI and that leads to severe breathing discomfort for people residing in the area. It was observed that the air quality level was worst during winter months (October to January).

Association between Ambient Air Pollution and Asthma Prevalence in Different Population Groups Residing in Eastern Texas, USA.

Air pollution has been an on-going research focus due to its detrimental impact on human health. However, its specific effects on asthma prevalence in different age groups, genders and races are not well understood. Thus, the present study was designed to examine the association between selected air pollutants and asthma prevalence in different population groups during 2010 in the eastern part of Texas, USA.The pollutants considered were particulate matter (PM2.5 with an aerodynamic diameter less than 2.5 micrometers) and surface ozone. The population groups were categorized based on age, gender, and race. County-wise asthma hospital discharge data for different age, gender, and racial groups were obtained from Texas Asthma Control Program, Office of Surveillance, Evaluation and Research, Texas Department of State Health Services. The annual means of the air pollutants were obtained from the United States Environmental Protection Agency (U.S. EPA)'s air quality system data mart program. Pearson correlation analyzes were conducted to examine the relationship between the annual mean concentrations of pollutants and asthma discharge rates (ADR) for different age groups, genders, and races. The results reveal that there is no significant association or relationship between ADR and exposure of air pollutants (PM2.5, and O3). The study results showed a positive correlation between PM2.5 and ADR and a negative correlation between ADR and ozone in most of the cases. These correlations were not statistically significant, and can be better explained by considering the local weather conditions. The research findings facilitate identification of hotspots for controlling the most affected populations from further environmental exposure to air pollution, and for preventing or reducing the health impacts.

An innovative approach for determination of air quality health index.

Fuzzy-analytical hierarchical process (F-AHP) can be extended to determine fuzzy air quality health index (FAQHI) for deducing health risk associated with local air pollution levels, and subjective parameters. The present work aims at determining FAQHI by considering five air pollutant parameters (SO2, NO2, O3, CO, and PM10) and three subjective parameters (population sensitivity, population density and location sensitivity). Each of the individual pollutants has varying impacts. Hence the combined health effects associated with the pollutants were estimated by aggregating the pollutants with different weights. Global weights for each evaluation alternatives were determined using fuzzy-AHP method. The developed model was applied to determine FAQHI in Howrah City, India from daily-observed concentrations of air pollutants over the three-year period between 2009 and 2011. The FAQHI values obtained through this method in Howrah City range from 1 to 3. Since the permissible value of FAQHI (as calculated for NAAQS) for residential areas is 1.78, higher index values are of public health concern to the exposed individuals. During the period of study, the observed FAQHI values were found to be higher than 1.78 in most of the day in the months of January to March, and October to December. However, the index values were below the recommended limit during rest of the months. In conclusion, FAQHI in Howrah city was above permissible limit in winter months and within acceptable values in summer and rainy months. Diurnal variations of FAQHI showed a similar trend during the three-year period of assessment.

A GIS based approach for assessing the association between air pollution and asthma in New York State, USA.

Studies on asthma have shown that air pollution can lead to increased asthma prevalence. The aim of this study is to examine the association between air pollution (fine particulate matter (PM2.5), sulfur dioxide (SO2) and ozone (O3)) and human health (asthma emergency department visit rate (AEVR) and asthma discharge rate (ADR)) among residents of New York, USA during the period 2005 to 2007. Annual rates of asthma were calculated from population estimates for 2005, 2006, and 2007 and number of asthma hospital discharge and emergency department visits. Population data for New York were taken from US Bureau of Census, and asthma data were obtained from New York State Department of Health, National Asthma Survey surveillance report. Data on the concentrations of PM2.5, SO2 and ground level ozone were obtained from various air quality monitoring stations distributed in different counties. Annual means of these concentrations were compared to annual variations in asthma prevalence by using Pearson correlation coefficient. We found different associations between the annual mean concentration of PM2.5, SO2 and surface ozone and the annual rates of asthma discharge and asthma emergency visit from 2005 to 2007. A positive correlation coefficient was observed between the annual mean concentration of PM2.5, and SO2 and the annual rates of asthma discharge and asthma emergency department visit from 2005 to 2007. However, the correlation coefficient between annual mean concentrations of ground ozone and the annual rates of asthma discharge and asthma emergency visit was found to be negative from 2005 to 2007. Our study suggests that the association between elevated concentrations of PM2.5 and SO2 and asthma prevalence among residents of New York State in USA is consistent enough to assume concretely a plausible and significant association.

An integrated WRF/HYSPLIT modeling approach for the assessment of PM(2.5) source regions over the Mississippi Gulf Coast region.

Fine particulate matter (PM(2.5)) is majorly formed by precursor gases, such as sulfur dioxide (SO(2)) and nitrogen oxides (NO(x)), which are emitted largely from intense industrial operations and transportation activities. PM(2.5) has been shown to affect respiratory health in humans. Evaluation of source regions and assessment of emission source contributions in the Gulf Coast region of the USA will be useful for the development of PM(2.5) regulatory and mitigation strategies. In the present study, the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model driven by the Weather Research & Forecasting (WRF) model is used to identify the emission source locations and transportation trends. Meteorological observations as well as PM(2.5) sulfate and nitric acid concentrations were collected at two sites during the Mississippi Coastal Atmospheric Dispersion Study, a summer 2009 field experiment along the Mississippi Gulf Coast. Meteorological fields during the campaign were simulated using WRF with three nested domains of 36, 12, and 4 km horizontal resolutions and 43 vertical levels and validated with North American Mesoscale Analysis. The HYSPLIT model was integrated with meteorological fields derived from the WRF model to identify the source locations using backward trajectory analysis. The backward trajectories for a 24-h period were plotted at 1-h intervals starting from two observation locations to identify probable sources. The back trajectories distinctly indicated the sources to be in the direction between south and west, thus to have origin from local Mississippi, neighboring Louisiana state, and Gulf of Mexico. Out of the eight power plants located within the radius of 300 km of the two monitoring sites examined as sources, only Watson, Cajun, and Morrow power plants fall in the path of the derived back trajectories. Forward dispersions patterns computed using HYSPLIT were plotted from each of these source locations using the hourly mean emission concentrations as computed from past annual emission strength data to assess extent of their contribution. An assessment of the relative contributions from the eight sources reveal that only Cajun and Morrow power plants contribute to the observations at the Wiggins Airport to a certain extent while none of the eight power plants contribute to the observations at Harrison Central High School. As these observations represent a moderate event with daily average values of 5-8 µg m(-3) for sulfate and 1-3 µg m(-3) for HNO(3) with differences between the two spatially varied sites, the local sources may also be significant contributors for the observed values of PM(2.5).

Air quality modeling for the urban Jackson, Mississippi Region using a high resolution WRF/Chem model.

In this study, an attempt was made to simulate the air quality with reference to ozone over the Jackson (Mississippi) region using an online WRF/Chem (Weather Research and Forecasting-Chemistry) model. The WRF/Chem model has the advantages of the integration of the meteorological and chemistry modules with the same computational grid and same physical parameterizations and includes the feedback between the atmospheric chemistry and physical processes. The model was designed to have three nested domains with the inner-most domain covering the study region with a resolution of 1 km. The model was integrated for 48 hours continuously starting from 0000 UTC of 6 June 2006 and the evolution of surface ozone and other precursor pollutants were analyzed. The model simulated atmospheric flow fields and distributions of NO2 and O3 were evaluated for each of the three different time periods. The GIS based spatial distribution maps for ozone, its precursors NO, NO2, CO and HONO and the back trajectories indicate that all the mobile sources in Jackson, Ridgeland and Madison contributing significantly for their formation. The present study demonstrates the applicability of WRF/Chem model to generate quantitative information at high spatial and temporal resolution for the development of decision support systems for air quality regulatory agencies and health administrators.

Environmental modeling, technology, and communication for land falling tropical cyclone/hurricane prediction.

Katrina (a tropical cyclone/hurricane) began to strengthen reaching a Category 5 storm on 28th August, 2005 and its winds reached peak intensity of 175 mph and pressure levels as low as 902 mb. Katrina eventually weakened to a category 3 storm and made a landfall in Plaquemines Parish, Louisiana, Gulf of Mexico, south of Buras on 29th August 2005. We investigate the time series intensity change of the hurricane Katrina using environmental modeling and technology tools to develop an early and advanced warning and prediction system. Environmental Mesoscale Model (Weather Research Forecast, WRF) simulations are used for prediction of intensity change and track of the hurricane Katrina. The model is run on a doubly nested domain centered over the central Gulf of Mexico, with grid spacing of 90 km and 30 km for 6 h periods, from August 28th to August 30th. The model results are in good agreement with the observations suggesting that the model is capable of simulating the surface features, intensity change and track and precipitation associated with hurricane Katrina. We computed the maximum vertical velocities (W(max)) using Convective Available Kinetic Energy (CAPE) obtained at the equilibrium level (EL), from atmospheric soundings over the Gulf Coast stations during the hurricane land falling for the period August 21-30, 2005. The large vertical atmospheric motions associated with the land falling hurricane Katrina produced severe weather including thunderstorms and tornadoes 2-3 days before landfall. The environmental modeling simulations in combination with sounding data show that the tools may be used as an advanced prediction and communication system (APCS) for land falling tropical cyclones/hurricanes.

Simulation of atmospheric dispersion of elevated releases from point sources in Mississippi Gulf Coast with different meteorological data.

Atmospheric dispersion calculations are made using the HYSPLIT Particle Dispersion Model for studying the transport and dispersion of air-borne releases from point elevated sources in the Mississippi Gulf coastal region. Simulations are performed separately with three meteorological data sets having different spatial and temporal resolution for a typical summer period in 1-3 June 2006 representing a weak synoptic condition. The first two data are the NCEP global and regional analyses (FNL, EDAS) while the third is a meso-scale simulation generated using the Weather Research and Forecasting model with nested domains at a fine resolution of 4 km. The meso-scale model results show significant temporal and spatial variations in the meteorological fields as a result of the combined influences of the land-sea breeze circulation, the large scale flow field and diurnal alteration in the mixing depth across the coast. The model predicted SO(2) concentrations showed that the trajectory and the concentration distribution varied in the three cases of input data. While calculations with FNL data show an overall higher correlation, there is a significant positive bias during daytime and negative bias during night time. Calculations with EDAS fields are significantly below the observations during both daytime and night time though plume behavior follows the coastal circulation. The diurnal plume behavior and its distribution are better simulated using the mesoscale WRF meteorological fields in the coastal environment suggesting its suitability for pollution dispersion impact assessment in the local scale. Results of different cases of simulation, comparison with observations, correlation and bias in each case are presented.