BACKGROUND: Soil-transmitted helminth (STH) is a group of helminths that are considered to be neglected tropical diseases (NTDs) and, globally, affect more than 1.5 to 2.6 million people yearly. Depending on the species, they can be acquired by ingesting embryonated eggs from contaminated matter or by skin penetration. Most species of STH are found in the tropics, such as the Philippines. Despite the Mass Drug Administration (MDA), the cases of STH infection continue to rise in the country. Surveys from the Research Institute of Tropical Medicine (RITM) indicate that a high prevalence of STH (Ascaris lumbricoides, Trichuris trichiura, and Necator americanus) was primarily observed in the provinces of the country, such as in Camarines Sur.
OBJECTIVE: To correlate remote sensing covariates such as Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI) - to STH-infected cases of the 37 municipalities of Camarines Sur.
METHODS: The available public health record of STH cases from 2015 to 2019 were calculated using the Quantum Geographic Information System (QGIS)and correlated using Pearson Correlation Coefficient.
RESULTS: The results showed that infection was higher in children than adults, and A. lumbricoides caused 60% of infection. No correlation of indices with infection cases during 2015 and 2017 was observed; however, 2019 showed a moderate strength (p = 0.037) in correlation.
CONCLUSIONS: This indicates that infection relied not mainly on vegetation and urbanization but on additional environmental factors and non-environmental variables.

Noise pollution is an unintentional consequence of mining activities, needing rigorous assessment, monitoring, and mitigation techniques to reduce its impact on local residents and ecosystems. The study specifically examines the noise pollution from rare earth mining activities in the Neendakara-Kayamkulam (NK) coastal belt, Kollam, Kerala, India, a region rich in ilmenite, rutile, sillimanite, zircon, and monazite. Despite the known environmental and health impacts of noise pollution, there is limited specific data on its magnitude and sources in this region, as well as a lack of effective mitigation strategies tailored to rare earth mining operations. Studies have indicated that mining operations, such as the movement of heavy mineral sands, considerably elevate noise levels, which have an effect on the environment\'s quality and public health. This study seeks to fill the gap by geospatial mapping and assessing the noise levels and recommend measures to effectively mitigate noise pollution. Systematic noise measurements were conducted at 48 suitable locations within the NK coastal belt, including residential, commercial, industrial, coastal, and silence zones. The noise levels vary from 49.1 dB(A) near a religious place to 82.4 dB(A) near the local industry. The study employs geospatial noise mapping and land cover superimposition to implement class-specific mitigation measures for noise pollution in a coastal vicinity mixed land use area, including natural and vegetative barriers, operational scheduling, zoning, and land use planning.

In general, land use and layout of streets can have a significant impact on the behavior of drivers and pedestrians. In particular, streetscape has often been overlooked that recognizing the role of streetscape on street accident in urban areas is important. The aim of this research is to investigate the influence of streetscape and land use on urban accidents that occurred in Mashhad between the years 2017 and 2021. To achieve this objective, the study focused on analyzing accidents in three different urban zones. It also considered the land use types adjacent to both closed and open streets, including residential, commercial, and mixed land uses. The research employed various surveys to gather the necessary data and insights related to the targeted areas. Statistics on accident in three zones show that among the mentioned land uses, commercial areas have experienced the highest number of accidents, with their share being approximately three times that of accidents in residential areas. Additionally, 75 % of all accidents took place in areas with open streetscape, whereas accidents in areas with enclosed view accounted for one third of the number of accidents in open streetscape areas. In this research, analysis and modeling were conducted using machine learning algorithms implemented in the Python programming language. Several models were employed, and the best models were selected based on their performance and accuracy, which include Random Forest Regression (RFR), Multilayer Neural Network Perceptron Regression (MLP) and Extreme Boost Gradient Regression (XGBoost). The accuracy of the machine learning models which successfully predicted future outcomes was as follows: Random Forest Regression (RFR) achieved 85 % accuracy, Extreme Boost Gradient Regression (XGBoost) achieved 81 % accuracy, and finally, Neural Network Multilayer Perceptron Regression (MLP) achieved 75 % accuracy.

Revealing the spatiotemporal evolution characteristics and key driving processes behind the habitat quality is of great significance for the scientific management of production, living, and ecological spaces in resource-based cities, as well as for the efficient allocation of resources. Focusing on the largest coal-mining subsidence area in Jiangsu Province of China, this study examines the spatiotemporal evolution of land use intensity, morphology, and functionality across different time periods. It evaluates the habitat quality characteristics of the Pan\'an Lake area by utilizing the InVEST model, spatial autocorrelation, and hotspot analysis techniques. Subsequently, by employing the GTWR model, it quantifies the influence of key factors, unveiling the spatially varying characteristics of their impact on habitat quality. The findings reveal a notable surge in construction activity within the Pan\'an Lake area, indicative of pronounced human intervention. Concurrently, habitat degradation intensifies, alongside an expanding spatial heterogeneity in degradation levels. The worst habitat quality occurs during the periods of coal mining and large-scale urban construction. The escalation in land use intensity emerges as the primary catalyst for habitat quality decline in the Pan\'an Lake area, with other factors exhibiting spatial variability in their effects and intensities across different stages.

Time constraints, financial limitations, and inadequate tools restrict the flood data collection in undeveloped countries, especially in the Asian and African regions. Engaging citizens in data collection and contribution has the potential to overcome these challenges. This research demonstrates the applicability of citizen science for gathering flood risk-related data on residential flooding, land use information, and flood damage to paddy fields for the Bui River Basin in Vietnam. Locals living in or around flood-affected areas participated in data collection campaigns as citizen scientists using self-investigation or investigation with a data collection app, a web form, and paper forms. We developed a community-based rainfall monitoring network in the study area using low-cost rain gauges to draw locals\' attention to the citizen science program. Fifty-nine participants contributed 594 completed questionnaires and measurements for four investigated subjects in the first year of implementation. Five citizen scientists were active participants and contributed more than 50 completed questionnaires or measurements, while nearly 50% of citizen scientists participated only one time. We compared the flood risk-related data obtained from citizen scientists with other independent data sources and found that the agreement between the two datasets on flooding points, land use classification, and the flood damage rate to paddy fields was acceptable (overall agreement above 73%). Rainfall monitoring activities encouraged the participants to proactively update data on flood events and land use situations during the data collection campaign. The study\'s outcomes demonstrate that citizen science can help to fill the gap in flood data in data-scarce areas.

In addressing environmental challenges and ecosystem resilience, green networks are preserved, repaired, and rebuilt by green infrastructure. However, urbanization effects have seen urban land form undergo significant modifications over time due to different anthropogenic activities. The objective of this study is to evaluate the land use and land cover (LULC) change in FESTAC Town, a government-owned residential neighborhood in Lagos, with the goal of recommending interventions for conserving green infrastructure. The study mainly focuses on employing remote sensing and geographic information system (GIS) techniques to detect alterations in land use in FESTAC Town from 1984 to 2022. The ERDAS Imagine software was utilized, employing a supervised classification-maximum likelihood algorithm, to identify changes in LULC. Additionally, an accuracy assessment was conducted using ground truth data. Findings from this study show significant increase in built-up areas at the cost of loss in dense vegetation over a 38-year period thereby, putting pressure on available green spaces. In terms of the area under each LULC category, most significant changes have been observed in built-up area (410.86%), bare surface (- 79.79%), sparse vegetation (- 53.42%), and dense vegetation (- 31.83%). Effective conservation strategies should focus on promoting connectivity between green spaces, engaging stakeholders in the planning and implementation of green infrastructure projects.

Evaluating the ecosystem health of riparian zones is helpful for decision-makers to formulate appropriate management measures. However, there are few methods for such evaluation which account for both the human requirements and ecological aspects of riparian zones. To address this, we created a Pressure-State(Vigor-Organization-Resilience)-Response framework for evaluating the ecosystem health of the riparian zone of the Yangtze River in Jiangsu Province, a region experiencing intense land use changes. Evaluation indicators, including land use change and ecosystem services, were selected. The comprehensive index method was used to calculate the evaluation indicators of ecosystem health, namely pressure, state, and response, and the comprehensive evaluation indicator itself. Using the cold and hot spot analysis, we also analyzed the spatial heterogeneity of ecosystem health in the riparian zone, constructed an ecological management pattern, and proposed corresponding management and protection measures. The results show that (1) from 2010 to 2020, construction land in the study area increased by more than 20%, and all studied land types underwent some degree of conversion to construction land, with cultivated land and water bodies being the main focus of conversion. (2) In 2020, the average ecosystem health in the riparian zone was normal, with a spatial distribution characterized by \"high dispersion and low clustering\"; and (3) according to the results of the ecosystem health evaluation and cold and hot spot analysis, key areas for stronger ecological protection were identified and, based on this, a number of management recommendations were proposed.

Identifying the spatial variability of soil quality indices is necessary to manage soil resources on a watershed scale. This study aimed to identify suitable indices for soil quality assessment at the watershed scale using various soil characteristics and modeling approaches. Another objective was to map soil quality variability in a representative area in the Qarasu watershed in Kermanshah province, west of Iran. Latin hypercube sampling method using the auxiliary variables used to select 163 sampling points based on land use, soil, and topographical variability in an area of about 57 thousand hectares. In the field operations, soil profiles were described, and samples were taken from different soil profile horizons. Soil properties such as texture, pH, salinity, available water, equivalent calcium carbonate, saturation percentage, soil organic carbon, nitrogen, available phosphorous, potassium, Fe, Zn, Cu and Mn, and soil aggregate stability (mean weight diameter (MWD), geometric mean diametric (GMD), and stable aggregates larger than 0.25 mm (WAS)) measured in the laboratory. Soil quality indices (productivity index (PI), soil quality index (SQI) and reduced dimension soil quality index using principal component analysis (SQI-PCA)) were calculated for each point using the measured soil properties. Soil quality indices were simulated using modeling with the random forest and support vector machine methods and auxiliary variables. Results showed that the range of soil characteristics and integrated soil quality indices was very high across the study area. Soil organic carbon percent varied from about 0.19 to 8.44%. The range of changes in PI in the study area was more than SQI and SQI-PCA indices. Quantities of all soil quality indices were higher in forest and rangeland than in agricultural lands. The spatial estimation accuracy of the SQI-PCA was higher than other soil quality indices and converged well with land use changes compared to PI and SQI.

Vegetation patterns on slopes strongly affect the water cycle processes in a basin, especially the water yield and confluence in arid areas. Quantifying and evaluating the effects of hydrological change on the migration and transformation of pollutants are challenging. Based on 4-year stream water quality data of 13 monitoring sites in the Huangshui River basin, a typical arid watershed of the Chinese Loess Plateau, the redundancy analysis (RDA) and structural equation modeling (SEM) analysis tools were used to quantify its relationship with vegetation patterns. In the study, land use and the enhanced vegetation index (EVI) were used as a metric of vegetation patterns; accordingly, the 13 catchments were divided into three groups via the cluster analysis, including large (over 80%), medium (70 ~ 80%), and small (below 70%) proportion vegetation patterns (LVP, MVP, SVP). The results of the LVP group showed that vegetation patterns negatively affected the contamination of total phosphorus (TP), ammonia nitrogen (NH3-N), permanganate index (CODMn), and biochemical oxygen demand (BOD5) in the stream water, and the contribution rates were - 0.57. While the proportion of urban area positively correlated with stream water quality in the groups of MVP and SVP, the contribution rates were 0.46 and 0.36, respectively. Moreover, the precipitation in the groups of MVP and SVP negatively correlated with pollutants (- 0.24 and - 0.26). Those results revealed the response of stream water quality to vegetation patterns on the slope with the consideration of precipitation, land use, and socio-economic factors for the regional water and land resource allocation. This study has important management implications for vegetation patterns on slope of fragile ecosystems in arid areas.

Terrestrialecosystems are significant carbon sinks and are crucial for understanding the regional and global carbon cycles, energy flow, and climate change. As land use change is a significant process affecting ecosystem carbon stocks and striving for land degradation neutrality (LDN), studying it is essential for comprehending the evolution of regional carbon sink functions and achieving sustainable development goals. The drastically diverse land use patterns in each of the study area\'s regions resulted in significant differences in carbon stock. This study explores the evolution traits of carbon stocks based on land use data and their driving mechanisms in Chongqing during the past 30 years by using spatial analysis, the InVEST model, and geographic probes. The results demonstrate that from 1990 to 2020, land degradation in Chongqing was made worse by the demand for land for construction land, but the strategy of converting cropland back to forests raised the carbon stock of forest land. The overall result is a decrease in total carbon stocks of 5.1078 Tg or 1.5%. The main pathway for carbon loss pathway in the evolution of carbon stock is the conversion of cropland to construction land, and the primary carbon compensation pathway is the conversion of grassland and cropland to forest land, with a spatial distribution characterized by \"higher in the whole area and obvious local differences.\" The land use intensity index has the most significant influence on the evolution of carbon stock. Moreover, the interaction of pairwise factors played a more important role in affecting the evolution of carbon stocks than did each factor individually. The case study in this paper shows that land use change is a significant driving mechanism for the evolution of carbon stock, and the development of a driving model theory is appropriate for deciphering the trajectory of carbon stock evolution and offering research suggestions for other regions.