Development of PLS–path model for understanding the role of precursors on ground level ozone concentration in Gulfport, Mississippi, USA

AbstractGround-level ozone (GLO) is produced by a complex chain of atmospheric chemical reactions that depend on precursor emissions from natural and anthropogenic sources. GLO concentration in a particular location is also governed by local weather and climatic factors. In this work an attempt was made to explore a Partial Least Squares Path Modeling (PLS–PM) approach to quantify the interrelationship between local conditions (weather parameters and primary air pollution) and GLO concentrations. PLS path modeling algorithm was introduced and applied to GLO concentration analyses at Gulfport, Mississippi, USA. In the present analysis, three latent variables were selected: PRC (photochemical reaction catalyst), MP (meteorological factor), and OPP (other primary air pollutants). The three latent variables included 14 indicators for the analysis; with PRC having two (extraterrestrial radiation on horizontal surface, and extraterrestrial radiation normal to the sun), MP having nine (temperature, dew point, relative humidity, pressure, visibility, maximum wind speed, average wind speed, precipitation, and wind direction) and OPP having three (NO2, PM2.5, and SO2) parameters. The resulting model revealed that PRC had significant direct impact on GLO concentration but very small overall effect. This is because PRC had significant indirect negative impact on GLO via MP. Thus, when both direct and indirect effects were taken into account, PRC emerged as having the weakest effect on GLO. The third variable (OPP) also had a positive impact on GLO concentration

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 (Wmax) 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 surface ozone pollution in the Central Gulf Coast region during summer synoptic condition using WRF/Chem air quality model

AbstractWRF/Chem, a fully coupled meteorology–chemistry model, was used for the simulation of surface ozone pollution over the Central Gulf Coast region in Southeast United States of America (USA). Two ozone episodes during June 8–11, 2006 and July 18–22, 2006 characterized with hourly mixing ratios of 60–100ppbv, were selected for the study. Suite of sensitivity experiments were conducted with three different planetary boundary layer (PBL) schemes and three land surface models (LSM). The results indicate that Yonsei–University (YSU) PBL scheme in combination with NOAH and SOIL LSMs produce better simulations of both the meteorological and chemical species than others. YSU PBL scheme in combination with NOAH LSM had slightly better simulation than with SOIL scheme. Spatial comparison with observations showed that YSUNOAH experiment well simulated the diurnal mean ozone mixing ratio, timing of diurnal cycle as well as range in ozone mixing ratio at most monitoring stations with an overall correlation of 0.726, bias of –1.55ppbv, mean absolute error of 8.11ppbv and root mean square error of 14.5ppbv; and with an underestimation of 7ppbv in the daytime peak ozone and about 8% in the daily average ozone. Model produced 1–hr, and 8–hr average ozone values were well correlated with corresponding observed means. The minor underestimation of daytime ozone is attributed to the slight underestimation of air temperature which tend to slow–down the ozone production and overestimation of wind speeds which transport the produced ozone at a faster rate. Simulated mean horizontal and vertical flow patterns suggest the role of the horizontal transport and the PBL diffusion in the development of high ozone during the episode. Overall, the model is found to perform reasonably well to simulate the ozone and other precursor pollutants with good correlations and low error metrics. Thus the study demonstrates the potential of WRF/Chem model for air quality prediction in coastal environments

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

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

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

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

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

Spatio-Temporal Variation of Particulate Matter(PM) 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 (PM 2.5 ) concentrations in a mega city, Delhi. The daily PM 2.5 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 PM 2.5 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 PM 2.5 air quality index (PM 2.5 —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 PM 2.5 -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)