Tropospheric ozone (O3) pollution is a major problem worldwide, including in the United States of America (USA), particularly during the summer months. Ozone oxidative capacity and its impact on human health have attracted the attention of the scientific community. In the USA, sparse spatial observations for O3 may not provide a reliable source of data over a geo-environmental region. Geostatistical Analyst in ArcGIS has the capability to interpolate values in unmonitored geo-spaces of interest. In this study of eastern Texas O3 pollution, hourly episodes for spring and summer 2012 were selectively identified. To visualize the O3 distribution, geostatistical techniques were employed in ArcMap. Using ordinary Kriging, geostatistical layers of O3 for all the studied hours were predicted and mapped at a spatial resolution of 1 kilometer. A decent level of prediction accuracy was achieved and was confirmed from cross-validation results. The mean prediction error was close to 0, the root mean-standardized-prediction error was close to 1, and the root mean square and average standard errors were small. O3 pollution map data can be further used in analysis and modeling studies. Kriging results and O3 decadal trends indicate that the populace in Houston-Sugar Land-Baytown, Dallas-Fort Worth-Arlington, Beaumont-Port Arthur, San Antonio, and Longview are repeatedly exposed to high levels of O3-related pollution, and are prone to the corresponding respiratory and cardiovascular health effects. Optimization of the monitoring network proves to be an added advantage for the accurate prediction of exposure levels.

In humid climates, waterlogging from excessive rainfalls can be a major limiting factor for soybean production, particularly during the reproductive stage. However, there is a limited understanding of how soybean growth and physiology respond to waterlogging during this critical stage. Here, we investigated the effects of waterlogging and subsequent reoxygenation on the growth, physiology, yields, and leaf hyperspectral reflectance traits of the soybean cultivar ‘Asgrow AG5332’. The crop was grown to stage R1 (initial flowering) in outdoor pot culture conditions, and then waterlogged for 16 days. The flooded pots were drained and continuously monitored for recovery for an additional 16 days. The results showed that soil oxygen levels declined rapidly to zero in about 5 days after waterlogging, and slowly recovered in about 5–16 days. However, it did not reach the same level as the control plants, which maintained an oxygen concentration of 18%. Increasing waterlogging duration negatively affected leaf chlorophyll index, water potential, and stomatal conductance, with a consequent decline in the photosynthetic rate. Further, decreased photosynthetic rate, leaf area, and mineral nutrients resulted in lower biomass and seed yield. Pod dry weight and leaf number were the most and least sensitive parameters, respectively, decreasing by 81% and 15% after 16 days of waterlogging. Waterlogged plants also had higher reflectance in the PAR, blue, green, and red regions, and lower reflectance in the near-infrared, tissue, and water band regions, indicating changes in chemistry and pigment content. The current study reveals that the soybean crop is susceptible to waterlogging during the reproductive stage, due to poor recovery of soil oxygen levels and physiological parameters. Understanding and integrating the growth, physiology, and hyperspectral reflectance data from this study could be used to develop improved cultivars to ensure the stability of soybean production in waterlogging-prone areas.

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.