BACKGROUND: Access to health care services is a critical determinant of population health and well-being. Measuring spatial accessibility to health services is essential for understanding health care distribution and addressing potential inequities.
OBJECTIVE: In this study, we developed a geoprocessing toolbox including Python script tools for the ArcGIS Pro environment to measure the spatial accessibility of health services using both classic and enhanced versions of the 2-step floating catchment area method.
METHODS: Each of our tools incorporated both distance buffers and travel time catchments to calculate accessibility scores based on users\' choices. Additionally, we developed a separate tool to create travel time catchments that is compatible with both locally available network data sets and ArcGIS Online data sources. We conducted a case study focusing on the accessibility of hemodialysis services in the state of Tennessee using the 4 versions of the accessibility tools. Notably, the calculation of the target population considered age as a significant nonspatial factor influencing hemodialysis service accessibility. Weighted populations were calculated using end-stage renal disease incidence rates in different age groups.
RESULTS: The implemented tools are made accessible through ArcGIS Online for free use by the research community. The case study revealed disparities in the accessibility of hemodialysis services, with urban areas demonstrating higher scores compared to rural and suburban regions.
CONCLUSIONS: These geoprocessing tools can serve as valuable decision-support resources for health care providers, organizations, and policy makers to improve equitable access to health care services. This comprehensive approach to measuring spatial accessibility can empower health care stakeholders to address health care distribution challenges effectively.

Landfilling is the least preferred method in the hierarchy of solid waste management options, but it is the most widely practiced option. Thus, identification of environmentally and economically suitable landfill sites should be of prime importance. The main objective of this study is to identify environmentally and economically suitable landfill sites using fuzzy analytical hierarchy process-based weighted linear combination model within a GIS environment. This study also used the DRASTIC-based groundwater vulnerability index and distance of landfills from densely populated areas to protect groundwater and reduce cost of transportation of solid waste which were not considered by the previous studies. Using the previously reported methods, a total of 132 landfill sites were found environmentally suitable in the study area. But, after applying DRASTIC-based groundwater vulnerability index, the number of environmentally suitable sites reduced to 95. When the proximity of the 95 sites to densely populated areas was considered to reduce waste transportation cost, the number of selected sites further reduced to 21 site and they can be considered the most environmentally and economically suitable landfill sites. This study will help the policy makers and the concerned SWM authorities to construct the engineered landfills at environmentally and economically suitable landfill sites in the study area and in other similar areas.

A sequence of dust intrusions occurred from the Sahara Desert to the central Mediterranean in the second half of June 2021. This event was simulated by means of the Weather Research and Forecasting coupled with chemistry (WRF-Chem) regional chemical transport model (CTM). The population exposure to the dust surface PM2.5 was evaluated with the open-source quantum geographical information system (QGIS) by combining the output of the CTM with the resident population map of Italy. WRF-Chem analyses were compared with spaceborne aerosol observations derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and, for the PM2.5 surface dust concentration, with the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis. Considering the full-period (17-24 June) and area-averaged statistics, the WRF-Chem simulations showed a general underestimation for both the aerosol optical depth (AOD) and the PM2.5 surface dust concentration. The comparison of exposure classes calculated for Italy and its macro-regions showed that the dust sequence exposure varies with the location and entity of the resident population amount. The lowest exposure class (up to 5 µg m-3) had the highest percentage (38%) of the population of Italy and most of the population of north Italy, whereas more than a half of the population of central, south and insular Italy had been exposed to dust PM2.5 in the range of 15-25 µg m-3. The coupling of the WRF-Chem model with QGIS is a promising tool for the management of risks posed by extreme pollution and/or severe meteorological events. Specifically, the present methodology can also be applied for operational dust forecasting purposes, to deliver safety alarm messages to areas with the most exposed population.

Predicting potential fire hazard zones in natural areas is one of the means of mitigating and managing fires. The current research focuses on the prioritizing of elements which contribute to the spread of fire and the special zoning of potentially dangerous areas in addition to the pinpointing of locations for the establishment of fire stations in forested areas in the Shimbar national reserve based on historical data spanning 2001 to 2018. The study utilizes elements (physiological, vegetation cover, meteorological, anthropological factors) contributing to wildfires as inputs into an artificial neural network and the development of a fuzzy inference system in order to produce fire zoning maps for the region under study. The map is divided into five sectors, i.e., minimum, low, moderate, high, and maximum risk of fire. The validation of the fire zoning map was evaluated at 0.83 and the RMSE error was 0.75. The results obtained show that 20% of the area under study is within the average risk category, 11% is within the high-risk category, and 10% is within the very high-risk category of a potential fire hazard. The most important variables were distance from a flowing source, i.e., river or stream, the land formation type, elevation, and the minimum temperature. The identification of suitable locations for firefighting stations was carried out by merging the fuzzy inference system model and Arc GIS, and the results obtained defined 16 possible locations. It was concluded that the application of hybrid models when dealing with the aforementioned variables is effective when seeking to determine locations for the establishment of firefighting stations and rural safety services; moreover, such hybrid models are highly efficacious for determining of fire hazard zones. It is proposed that hybrid models be applied on a large scale for the prevention, control, and management of fires throughout the country.

(1) Background: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease with still unknown etiology. Some occupational and environmental risk factors have been suggested, including long-term air pollutant exposure. We carried out a pilot case-control study in order to evaluate ALS risk due to particulate matter with a diameter of ≤10 µm (PM10) as a proxy of vehicular traffic exposure. (2) Methods: We recruited ALS patients and controls referred to the Modena Neurology ALS Care Center between 1994 and 2015. Using a geographical information system, we modeled PM10 concentrations due to traffic emissions at the geocoded residence address at the date of case diagnosis. We computed the odds ratio (OR) and 95% confidence interval (CI) of ALS according to increasing PM10 exposure, using an unconditional logistic regression model adjusted for age and sex. (3) Results: For the 132 study participants (52 cases and 80 controls), the average of annual median and maximum PM10 concentrations were 5.2 and 38.6 µg/m3, respectively. Using fixed cutpoints at 5, 10, and 20 of the annual median PM10 levels, and compared with exposure <5 µg/m3, we found no excess ALS risk at 5-10 µg/m3 (OR 0.87, 95% CI 0.39-1.96), 10-20 µg/m3 (0.94, 95% CI 0.24-3.70), and ≥20 µg/m3 (0.87, 95% CI 0.05-15.01). Based on maximum PM10 concentrations, we found a statistically unstable excess ALS risk for subjects exposed at 10-20 µg/m3 (OR 4.27, 95% CI 0.69-26.51) compared with those exposed <10 µg/m3. However, risk decreased at 20-50 µg/m3 (OR 1.49, 95% CI 0.39-5.75) and ≥50 µg/m3 (1.16, 95% CI 0.28-4.82). ALS risk in increasing tertiles of exposure showed a similar null association, while comparison between the highest and the three lowest quartiles lumped together showed little evidence for an excess risk at PM10 concentrations (OR 1.13, 95% CI 0.50-2.55). After restricting the analysis to subjects with stable residence, we found substantially similar results. (4) Conclusions: In this pilot study, we found limited evidence of an increased ALS risk due to long-term exposure at high PM10 concentration, though the high statistical imprecision of the risk estimates, due to the small sample size, particularly in some exposure categories, limited our capacity to detect small increases in risk, and further larger studies are needed to assess this relation.

Despite environmental regulations in Ecuador, particularly in the Province of Azuay, the solid waste final disposal management is still a socio-environmental problem, worsened by weak governance processes. The province has three sanitary landfills with almost expired service lives. The site selection was based on circumstantial reasons, which makes landfills more likely to cause environmental pollution and, therefore, have negative implications for public health. The largest landfill serves Cuenca and also leases service to other small cities. The remaining two are small and, accordingly, have limited technology and fewer resources. In this context, the main aim of this study is to evaluate the terrain of the province to find the most suitable area for landfill siting. A multi-criteria decision analysis, integrated with a geographical information system and analytical hierarchy process methodology, was conducted. Fourteen factors and seven constraints were simultaneously analysed, divided into technical, environmental, social, and economic categories; 15 of these criteria were from the Ecuadorian Unified Text of the Secondary Legislation of the Environmental Ministry. According to the results, 76.17% of the territory is not suitable for landfill implementation, and the unrestricted area represents the remaining 23.83%. The highest landfill suitability index (70-81%) is located in the south of the province in Santa Isabel, Oña, and Nabón cantons, which are dry and clay-rich areas.

In this study, the irrigation water quality of Eryaman region in Ankara (Turkey) has been investigated using a Geographic Information System-Multicriteria Decision Analysis (GIS-MCDA)-based model. Two different irrigation water quality indices (IWQI-A and IWQI-B) based on Analytic Hierarchy Process (AHP) and Data Envelopment Analysis (DEA) have been developed to provide a single suitability score for all criteria of irrigation water quality. The irrigation water quality indices were composed of 3 main criteria and 11 sub-criteria. Based on irrigation water quality indices, four suitability classes have been identified as (a) excellent, (b) good, (c) permissible, and (d) unsuitable. Irrigation water quality maps were created by using kriging method and the water quality index scores. The temporal variation of irrigation water quality was evaluated using the models created for July 2007, 2010, and 2015 years. The most suitable areas for irrigation water are concentrated in the eastern and western parts of the region where the IWQI-B scores were >0.85. In recent years, the deterioration in irrigation water quality has increased in the southern parts of the basin. This research has demonstrated that the indices created by using DEA and AHP methods are reliable indicators to assess irrigation water quality. PRACTITIONER POINTS: Water quality indices can be used to depict the overall water quality status in water management planning. To avoid problems when modeling groundwater quality, the factors that have the most effect on water quality should be selected. Analytic Hierarchy Process and Data Envelopment Analysis techniques are efficient and reliable methods to determine water quality.

One of the important issues in the world is the significant growth of waste production, including waste that is not biodegradable in nature. According to the Kerman Municipality, 440 tonnes of municipal waste is collected daily in Kerman consisting of five major parts of paper, plastic, metal, glass, and wet waste. The major problems of municipal solid waste disposal are soil erosion, air pollution, and greenhouse gas emissions. The most important factors related to recycling are waste sorting and the relevant environmental conditions. This study aims to create a sustainable approach by locating the optimal sites to reduce environmental pollution, decrease costs, and improve the service system to the society. Optimal locations for establishing the collecting and sorting centers in the city are specified by the use of geographic information system software, based on criteria consisting of population density, road network, distance to health centers, distance to disposal center, waste sorting culture, land space, and land cost, which were weighted by an analytical hierarchy process. It was noteworthy that the criterion \"waste sorting culture\", which has a foundation in human sciences and sociology, has been considered by experts in this study to be of the highest importance among other criteria at locating sorting centers. Subsequently, using a symmetric capacitated vehicle routing problem, the number and capacity of each vehicle are determined to serve the specified locations according to the economic, social, and environmental constraints.

Kemalpaşa district (İzmir/Turkey), a significant area in terms of its natural and cultural characteristics alongside with its agricultural production, was selected as the specific site for the study at hand as its natural resources were endangered as a result of unplanned industrial settlement and development in the area. Therefore, the aim of the study was to ascertain the most suitable industrial sites for the area by taking natural factors into consideration, while excluding the economic ones. Within the scope of the study, a total of 13 criteria were set including wildlife development areas, protected areas, forest lands, olive groves, pastures, agricultural lands, water surfaces, streams, land capability areas, distance to residential areas, transportation (distance to roads), slopes, and erosion. Seventeen maps were created based on these 13 criteria and were used to select the suitable sites. The inquiries were conducted through the weighted linear combination technique and the analytic hierarchy process method utilizing the geographical information system software ArcGIS 10.2.1. The land use of Kemalpaşa is classified under five classes indicating different suitability values for industrial use and evaluating the land from \"not suitable\" (0) to \"most suitable\" (4). The results of the study revealed that 98.64% of Kemalpaşa district was \"not suitable\" (0) for industrial land use. The results further indicated that only 0.50% of the district was \"suitable\" (3) for industrial use, while 0.86% was found to be \"most suitable\" (4) to that end. Three alternative sites designated by the study and current industrial sites were evaluated based on protection criteria and planning proposals were suggested.