Pesticide toxicity assessment and geographic information system (GIS) application in small-scale rice farming operations, Thailand

The objectives of the study were to evaluate the impact of pesticide exposure on farmer health during non-active rice farming and active rice farming periods and present the change in the individual cholinesterase activities (%reduction) on the geographic information system (GIS) mapping in Nakhon Ratchasima Province, Thailand. Acetyl- and butyryl-cholinesterase (AChE and BuChE) activities were monitored during both study periods using Test-mate ChE (Model 400). The location of paddy fields was specified using Garmin geographic positioning system MAP 62s. Fifty-eight farmers who participated in this study had an average age of 49.2 ± 6.9 years. Higher prevalence of all health symptoms was observed among farmer participants during the active rice farming period comparing to the non-active rice farming period (p < 0.01). Furthermore, farmers had significantly lower activities of AChE and BuChE during the active rice farming period comparing to the non-active rice farming period (p < 0.01). Our findings indicate that the GIS mapping indicate that the cases with a significant enzyme inhibition have dispersed across the agricultural and the nearby residential areas. This, investigation can be used to promote safer use of pesticides among farmers and mitigate pesticide exposure among residents living in close proximity to a rice field.

GIS Mapping of Short-Term Noisy Event of Diwali Night in Lucknow City

Noise is a universal problem that is particularly prominent in developing nations like India. Short-term noise-sensitive events like New Year’s Eve, derby matches, DJ night, Diwali night (celebration with firecracker) in India, etc. create lots of noise in a short period. There is a need to come up with a system that can predict the noise level for an area for a short period indicating its detailed variations. GIS (Geographic Information System)-based google maps for terrain data and crowd-sourced or indirect collection of noise data can overcome this challenge to a great extent. Authors have tried to map the highly noisy Diwali night for Lucknow, a northern city of India. The mapping was done by collecting the data from 100 points using the noise capture app (30% were close to the source and 70% were away from the source (receiver). Noise data were predicted for 750 data points using the modeling interpolation technique. A noise map is generated for this Diwali night using the crowd-sourcing technique for Diwali night. The results were also varied with 50 test points and are found to be within ±4.4 dB. Further, a noise map is also developed for the same site using indirect data of noise produced from the air pollution open-sourced data. The produced noise map is also verified with 50 test points and found to be ±6.2 dB. The results are also corroborated with the health assessment survey report of the residents of nearby areas.

Rainfed and Supplemental Irrigation Modelling 2D GIS Moisture Rootzone Mapping on Yield and Seed Oil of Cotton (Gossypium hirsutum) Using Precision Agriculture and Remote Sensing

The effects of two irrigation (IR1: rainfed; IR2: rainfed + supplemental drip irrigation), and two fertilization (Ft1, Ft2) treatments were studied on cotton yield and seed oil by applying a number of new agro-technologies such as: TDR sensors; soil moisture (SM); precision agriculture; remote-sensing NDVI (Sentinel-2 satellite sensor); soil-hydraulic analyses; geostatistical models; SM-rootzone, and modelling 2D GIS mapping. A daily soil-water-crop-atmosphere (SWCA) balance model was developed. The two-way ANOVA statistical analysis results revealed that irrigation (IR2 = best) and fertilization treatments (Ft1 = best) significantly affected yield and oil content. Supplemental irrigation, if applied during critical growth stages, could result in substantial improvement on yield (+234.12%) and oil content (+126.44%).

Soil Moisture Depletion Modelling Using a TDR Multi-Sensor System, GIS, Soil Analyzes, Precision Agriculture and Remote Sensing on Maize for Improved Irrigation-Fertilization Decisions

The effects of three drip irrigation (IR1: Farmer’s, IR2:Full (100%ETc), IR3:Deficit (80%ETc) irrigation), and two fertilization (Ft1, Ft2) treatments were studied on maize yield and biomass by applying new agro-technologies (TDR—sensors for soil moisture (SM) measurements, Precision Agriculture, Remote Sensing—NDVI (Sentinel-2 satellite sensor), soil-hydraulic analyses and Geostatistical models, SM-rootzone modelling-2D-GIS mapping). A daily soil moisture depletion (SMDp) model was developed. The two-way-ANOVA statistical analysis results revealed that irrigation (IR3 = best) and fertilization treatments (Ft1 = best) significantly affect yield and biomass. Deficit irrigation and proper fertilization based on new agro-technologies for improved management decisions can result in substantial improvement on yield (+116.10%) and biomass (+119.71%) with less net water use (−7.49%) and reduced drainage water losses (−41.02%).

Health Disparities and Climate Change: The Intersection of Three Disaster Events on Vulnerable Communities in Houston, Texas

Although evidence suggests that successive climate disasters are on the rise, few studies have documented the disproportionate impacts on communities of color. Through the unique lens of successive disaster events (Hurricane Harvey and Winter Storm Uri) coupled with the COVID-19 pandemic, we assessed disaster exposure in minority communities in Harris County, Texas. A mixed methods approach employing qualitative and quantitative designs was used to examine the relationships between successive disasters (and the role of climate change), population geography, race, and health disparities-related outcomes. This study identified four communities in the greater Houston area with predominantly non-Hispanic African American residents. We used data chronicling the local community and environment to build base maps and conducted spatial analyses using Geographic Information System (GIS) mapping. We complemented these data with focus groups to assess participants’ experiences in disaster planning and recovery, as well as community resilience. Thematic analysis was used to identify key patterns. Across all four communities, we observed significant Hurricane Harvey flooding and significantly greater exposure to 10 of the 11 COVID-19 risk factors examined, compared to the rest of the county. Spatial analyses reveal higher disease burden, greater social vulnerability, and significantly higher community-level risk factors for both pandemics and disaster events in the four communities, compared to all other communities in Harris County. Two themes emerged from thematic data analysis: (1) Prior disaster exposure prepared minority populations in Harris County to better handle subsequent disaster suggesting enhanced disaster resilience, and (2) social connectedness was key to disaster resiliency. Long-standing disparities make people of color at greater risk for social vulnerability. Addressing climate change offers the potential to alleviate these health disparities.

Perceptual mapping of linguistic variation in Saudi Arabic dialects

This article provides a perceptual dialectology account of linguistic diversity in Saudi Arabia. Using the map-drawing and labeling task, the study examined the perceptions and ideologies of 674 speakers of Saudi Arabic dialects about the perceived boundaries of regional dialect varieties, as well as their social evaluation of and beliefs about the dialects. The analysis of the results as displayed in composite maps using a Geographic Information System (GIS) mapping program revealed that respondents identified five major dialect areas as having the most distinct features: the Najdi, Hijazi, southern, eastern, and northern regions. Ten categories of respondents’ labels emerged out of the qualitative analysis: style, influence, Bedouin/urban, fast, open/closed, vowel lengthening, unique vocabulary, alternation of /k/ and /g/, attraction, and social media. The present findings show the salience of certain linguistic and social features that respondents associate with certain dialect areas. Such perceptions can ultimately guide and enhance future descriptions and analyses of actual linguistic variation in Saudi Arabia.