Moderate Risk Areas Research Articles

Mitigating flood risk at Shah Alam, Malaysia for sustainable development

The natural environment and infrastructure are severely damaged by floods, which are becoming frequent occurrences. It may not be able to stop floods given the impending climate change and variability of the weather. Yet mapping flood risk can help with both mitigation and prevention of flooding. The Shah Alam experiences flash floods yearly, which is why it was chosen. An interactive approach to decision-making under multi-criteria decision analysis (MCDA), the analytical hierarchy process (AHP), is used. The flood risk zone is divided into five categories very low-risk (32.38%), low-risk (22.25%), moderate-risk (23.17%), high-risk (18.87%), and very high-risk (3.33%) respectively. Based on the data gathered for this study, the economic losses are identified, low-risk areas (RM 2091), moderate-risk areas (RM 2343), and high-risk areas (RM 2728). The historical flood events occur in the Seksyen 36, 18, 17, and most of the southern part of Shah Alam, Malaysia. Rainfall and the Klang River have the biggest effects on these regions, with water drainage systems being critical in the Shah Alam area to reduce the risk of floods. When analysing flood risks in the Shah Alam region and throughout the nation, important stakeholders both state and non-state actors can benefit greatly from the flood susceptibility map provided by this study.

Geospatial Modelling of Flood Susceptibility of the Calabar City, Cross River State, Nigeria

Flooding remains a major environmental problem in many parts of the world including Nigeria, causing untold losses. This study modelled the flood susceptibility of Calabar Metropolis using frequency ratio and geographical information system (GIS). Direct field observation and key informant approaches were used to obtain 127 previous and current flood locations in the study area from which a database of historical flood occurrence was developed. Using the geostatistical analyst tool within a geographical information environment the flood locations were split into two parts in the ratio of 70:30 percent for the training and testing processes, respectively. Eight flood conditioning factors (elevation, slope, aspect, curvature topographic position index, topographic wetness index, land cover and normalized difference vegetation index) were extracted from SRTM-30m DEM and Landsat 8 data accordingly and used in the geospatial analysis of flood occurrence within the GIS. The frequency ratio model was then developed using the training dataset. The final product was the flood susceptibility map of the study area. The results revealed that 9.3 percent of the study area was considered to have very high flood susceptibility, 19.97 percent was classified as high flood susceptibility while 31.79 percent was under moderate risk area. About 39 percent of the study area was classified at least under low flood susceptibility. The order of contribution of the conditioning factors to the model were topographic position index (TPI), elevation, normalized difference vegetation index (NDVI), land cover, curvature, aspect, slope, and topographic wetness index (TWI). The validation of the model using the receiver operating characteristics (ROC) curve returned an AUC value of 74.81 percent. Hence, the result was acceptable for the prediction of flood locations in the study area. The findings would assist urban planners, environmental managers, risk reduction and management agencies, land developers, policy makers, and indeed the general public.

Prediction of current and future gully erosion susceptibility in the Oued Zat watershed in the High Atlas region of Morocco using an SVM-based machine learning approach

Gully erosion constitutes a major natural hazard causing significant land degradation. Here, we aimed to assess the current and future susceptibility of gully erosion in the Oued Zat watershed (High Atlas, Morocco) using machine learning (ML). Leveraging 210 gully and 400 non-gully points along with 16 conditioning factors from precipitation, remote sensing, geological, and soil data, we employed five support vector machine (SVM) models based on linear, radial, and polynomial kernels for gully susceptibility modeling. Performance evaluation included specificity, recall, receiver operating characteristic area under the curve (ROC–AUC), precision, and F1 score metrics. Future vulnerability in the 21st century was assessed under two shared socioeconomic pathways (SSP2-4.5 and SSP5-8.5) climate scenarios. Our results demonstrated superior performance by SVM-Poly (recall: 0.93; AUC–ROC: 0.92; precision: 0.89; F1-Score: 0.91) followed by SVM-RBF (recall: 0.84; AUC–ROC: 0.86; precision: 0.78; F1-Score: 0.81), while linear models were the least accurate. Current predictions showed a prevalence of high- and very high-risk areas to gullying, together accounting for over 38% of the watershed, particularly in the south, while low- and very low-risk areas (26%) dominated in the north. Future projections anticipate declining very low, low, and moderate-risk areas. Initially, high- and very high-risk zones will increase in extent, followed by a gradual decline in very high-risk areas. In contrast, high-risk areas will continue increasing, reaching around 20% and 40% by 2100 under SSP2-4.5 and SSP5-8.5, respectively. Our findings not only reveal vulnerability to erosion but also demonstrate a practical, effective approach for decision-making, especially considering the impacts of climate change in the region.

Classification of Composite Hazards of Gas and Coal Spontaneous Combustion in Gob

ABSTRACT To prevent the occurrence of gas and coal spontaneous combustion composite disasters in gob areas during coal mining, this study considers the gob area of the Longfeng Coal Mine as the experimental background. By analyzing the pressure values of the hydraulic props in the isolation wall, the composition of gases in the gob area, and the low-temperature oxidation characteristics of coal, it was found that the pressure values of the hydraulic props were correlated with the concentration of gases in the gob area, temperature, and the g-factor and free radical concentration of coal. The absolute values of the correlation coefficients were ranked as T > O2 > CH4 > CO2 > g-values > Ng > N2, and all correlation coefficients were greater than 0.85. When the isolation wall was located in the stress concentration area, the wall deformed and cracked, leading to frequent gas exchange between the gob-side entry retaining (GSER) and the gob area, which increased the risk of gas and coal spontaneous combustion composite disasters in the gob area. According to the safety risk level, the areas adjacent to the gob area were divided into high-risk areas (0 ~ 100 m, 300 ~ 600 m, 700 ~ 900 m, 1050 ~ 1200 m), moderate risk areas (200 ~ 300 m, 900 ~ 1050 m), general risk areas (600 ~ 700 m), and low-risk areas (100 ~ 200 m), the positions at 500 ~ 550 m and 700 ~ 750 m were located in the gas and coal spontaneous combustion composite disaster area. The study suggests that for high-risk areas, the injection amount of fire extinguishing materials should be increased to strengthen safety assurance, whereas for low-risk areas, the injection can be moderately adjusted and reduced to achieve the optimal balance between economic benefits and safety benefits.

Spatial landslide risk assessment in a highly populated Rohingya refugee settlement area of Cox’s Bazar, Bangladesh

ABSTRACT In recent times, landslides have become a major concern in the southeast part of Bangladesh. This study aims to develop comprehensive landslide risk mapping by applying the analytical hierarchy process (AHP) and geospatial techniques in Ukhiya and Teknaf Upazilas, a highly populated Rohingya Refugee Settlement area. To assess the landslide risk, we selected 12 influencing criteria of hazard, vulnerability and exposure under the relevant components of risk, such as precipitation intensity, landslide inventory, distance to fault line, stream density, distance to stream network, elevation, aspect, slope, geology, normalized difference vegetation index (NDVI), land use and land cover (LULC), and population density. These spatial criteria were weighted using the AHP model. The weighted overlay techniques were then used to produce the landslide risk map. The findings demonstrate that 2.19% of the total area is classified as a very high risk zone, and 12.74% is categorized as a high risk zone. Moderate risk areas cover 23.08% of the total area. The risk map is validated by the landslides inventory. The outcomes can be used by the concerned authorities to take the necessary steps to reduce the impact of landslides.

Research on electric vehicle load forecasting considering regional special event characteristics

With the rise of electric vehicles and fast charging technology, electric vehicle load forecasting has become a concern for electric vehicle charging station planners and operators. Due to the non-stationary nature of traffic flow and the instability of the charging process, it is difficult to accurately predict the charging load of electric vehicles, especially in sudden major events. In this article, We proposes a high-precision EV charging load forecasting model based on mRMR and IPSO-LSTM, which can quickly respond to the epidemic (or similar emergencies). Firstly, the missing data in the original EV charging load data are supplemented, and the abnormal data are corrected. Based on this, a factor set is established, which included five epidemic factors, including new confirmed cases, the number of moderate risk areas, the number of high risk areas, epidemic situation and epidemic prevention policies of the city, and other factors such as temperature. Secondly, the processed load data and other data in the influencing factor set are normalized, and the typical characteristic curve is established for personalized processing of the relevant data of epidemic factors, so as to improve the sensitivity of load response to epidemic changes and the ability to capture special data (peak and valley values and turning points of load). Then the maximum relevant minimum redundancy (mRMR) is used to select the optimal feature set from the set of influencing factors. Then, the processed load data and its corresponding optimal selection are input into the IPSO-LSTM model to obtain the final prediction result. Finally, taking the relevant data of EV charging load in a city in China from November 2021 to April 2022 (the city experienced two local epidemics in December 2021 and March 2022 respectively) as an example, the model is evaluated and compared with other models under the forecast period of 1 h. Meanwhile, the performance of the model under different foresight periods (2 h, 4 h, 6 h) is compared and analyzed. The results show that the model has good stability and representativeness, and can be used for EV charging load prediction under the COVID-19 pandemic.

Common Features of Environmental Mycobacterium chelonae from Colorado Using Partial and Whole Genomic Sequence Analyses

Nontuberculous mycobacteria (NTM) are environmentally acquired opportunistic pathogens that cause chronic lung disease in susceptible individuals. While presumed to be ubiquitous in built and natural environments, NTM environmental studies are limited. While environmental sampling campaigns have been performed in geographic areas of high NTM disease burden, NTM species diversity is less defined among areas of lower disease burden like Colorado. In Colorado, metals such as molybdenum have been correlated with increased risk for NTM infection, yet environmental NTM species diversity has not yet been widely studied. Based on prior regression modeling, three areas of predicted high, moderate, and low NTM risk were identified for environmental sampling in Colorado. Ice, plumbing biofilms, and sink tap water samples were collected from publicly accessible freshwater sources. All samples were microbiologically cultured and NTM were identified using partial rpoB gene sequencing. From these samples, areas of moderate risk were more likely to be NTM positive. NTM recovery from ice was more common than recovery from plumbing biofilms or tap water. Overall, nine different NTM species were identified, including clinically important Mycobacterium chelonae. MinION technology was used to whole genome sequence and compare mutational differences between six M. chelonae genomes, representing three environmental isolates from this study and three other M. chelonae isolates from other sources. Drug resistance genes and prophages were common findings among environmentally derived M. chelonae, promoting the need for expanded environmental sampling campaigns to improve our current understanding of NTM species abundance while opening new avenues for improved targeted drug therapies.

A Risk Analysis of Landslides on the Morawaka “Kanda” Area in the Matara District of Sri Lanka

The Morawaka Kanda area has become heavily impacted by the landslides in the recent years resulting in socio-economic and environmental damages. This area which consists of steep slopes, soft soil layers with feldspar, poor drainage, constructions, deforestation, settlements and gardens may be at high potential risk for landslides. Therefore, in this study, special attention has been drawn to examine the risks of landslides on the Morawaka “Kanda” area in the Matara district. The research framework of this study was planned based on field surveys for the data collection of landslide risks using a data collection sheet and questionnaire. Satellite data taken by survey department was used for the risk area mapping using Geographical Information Systems (GIS). According to the analysis of causative factors, high risky land area is about 18 km2 and it is 24% of the area. The land extent of moderate and low risk is nearly 64% of the total area in this study area. The moderate landslide risk areas covered 24.14km2 land. It is about 31%. 25km2 is about low risk. 45% is low and no risk. Still there is a risk for future landslides in the unstable slope on the right bank of the Morawaka Kanda landslide path in 2017. 55% of the families live in the high risk area and only 20% of the families are in the moderate risk while 25% of the families have low or no risk. 50% of the high risk families are in Morawaka Kanda and Yati Horagala. The identification of landslide prone areas play an important role in avoiding or minimizing hazards and the identification of potential landslide risk areas is very important to ensure the safety of lives and property.

A Novel Estimation of the Composite Hazard of Landslides and Flash Floods Utilizing an Artificial Intelligence Approach

Landslides and flash floods are significant natural hazards with substantial risks to human settlements and the environment, and understanding their interconnection is vital. This research investigates the hazards of landslides and floods in two adopted basins in the Yamaguchi and Shimane prefectures, Japan. This study utilized ten environmental variables alongside categories representing landslide-prone, non-landslide, flooded, and non-flooded areas. Employing a machine-learning approach, namely, a LASSO regression model, we generated Landslide Hazard Maps (LHM), Flood Hazard Maps (FHM), and a Composite Hazard Map (CHM). The LHM identified flood-prone low-lying areas in the northwest and southeast, while central and northwest regions exhibited higher landslide susceptibility. Both LHM and FHM were classified into five hazard levels. Landslide hazards predominantly covered high- to moderate-risk areas, since the high-risk areas constituted 38.8% of the study region. Conversely, flood hazards were mostly low to moderate, with high- and very high-risk areas at 10.49% of the entire study area. The integration of LHM and FHM into CHM emphasized high-risk regions, underscoring the importance of tailored mitigation strategies. The accuracy of the model was assessed by employing the Receiver Operating Characteristic (ROC) curve method, and the Area Under the Curve (AUC) values were determined. The LHM and FHM exhibited an exceptional AUC of 99.36% and 99.06%, respectively, signifying the robust efficacy of the model. The novelty in this study is the generation of an integrated representation of both landslide and flood hazards. Finally, the produced hazard maps are essential for policymaking to address vulnerabilities to landslides and floods.

Preliminary Risk Assessment of Geological Disasters in Qinglong Gorge Scenic Area of Taihang Mountain with GIS Based on Analytic Hierarchy Process and Logistic Regression Model

Qinglong Gorge Scenic Area (QGSA) boasts stunning natural landscapes, characterized by towering peaks and extensive cliffs. Nevertheless, the intricate geological backdrop and distinctive topographical conditions of this area give rise to various geological disasters, posing a substantial safety concern for tourists and presenting ongoing operational and safety management challenges for the scenic area. In light of these challenges, this study placed its focus on the geological disasters within QGSA and sought to assess risks across various scales. The assessment was accomplished through a combination of methods, including field surveys conducted in 2022, remote sensing interpretation, and comprehensive data collection and organization. For the geological disaster risk assessment of the scenic area, this research selected seven key indicators, encompassing terrain factors, geological elements, structural characteristics, and other relevant factors. The assessment utilized a logistic regression model, which yielded satisfactory results with an AUC value of 0.8338. Furthermore, a model was constructed incorporating seven indicators, encompassing factors such as population vulnerability, material susceptibility, and the vulnerability of tourism resources. To assess vulnerability to geological disasters, the Analytic Hierarchy Process (AHP) was employed, resulting in a CR of 0, thus ensuring the reliability of the findings. The outcomes of the risk assessment indicate that the low-risk area covers a substantial expanse of 5.45 km2, representing 53.66% of the total area. The moderate-risk area extends over 3.59 km2, constituting 35.43%, while the high-risk area encompasses 0.72 km2, accounting for 7.14%. Additionally, the very high-risk area encompasses 0.38 km2, making up 3.77% of the total area. Consequently, building upon the findings of the risk assessment, this paper introduces a risk classification and control prevention system. This system provides invaluable insights for disaster prevention and control in mountainous and canyon-type scenic areas.

Unveiling the Nexus the link between water quality index and phthalic acid ester concentrations in Tigris River

Monitoring water quality in an aquatic system is a crucial objective in pursuing sustainable development, despite the challenges posed by water scarcity and the impact of climate change. The objective of the present study was to examine the water quality of the Tigris River along its course from the point of entry into Baghdad city to its exit, particularly in the presence of Phthalic Acid Esters (PAEs). In both dry and wet seasons and from five sites of the river, nine environmental factors (Turbidity, Total dissolved solids, pH, dissolved oxygen, Chloride, nitrate, phosphate, sulfate, and Biological oxygen demand) and three PAEs (di-n-butyl phthalate (DBP), di-isononyl phthalate (DiNP), and di-2-ethyl hexyl phthalate (DEHP)) were investigated. Results revealed that the river has high levels of turbidity and Total dissolved solids, slightly alkaline, and well-aerated (dissolved oxygen >7 mg/L). Chloride, nitrate, phosphate, and sulfate values varied between seasons, as high values were recorded in the wet season. Biochemical oxygen demand levels were higher during the dry season. The highest value of total PAEs (2.20 μg/L) was observed during the dry season, and the lowest value was in the wet season (1.16 μg/L). The HQ results revealed that the river was ranked as a moderate risk area. However the water quality index and environmental risk index classified the water quality as a poor category, with a high-risk score in the study sites within Baghdad city. Therefore, despite the acceptable limit of PAEs in the river's water (<3 μg/L), its presence is an early alarm to withdraw attention to its environmental risk. These findings highlight the need for appropriate measures to control and remove pollutants to ensure a safe and healthy water supply in the Tigris River.

A novel ecological risk assessment approach applied to Jiangsu coastal zone, China

Natural and anthropogenic risk factors contribute to ecological risks in coastal zones; however, the response of ecological entities (receptors) to disturbances varies with the risk source. We used the risk causal law as a guide to construct an ecological risk assessment method for Jiangsu coastal zone, China, with different ecological receptors as the research subjects. We screened the evaluation indicators for natural and anthropogenic risks, validated the evaluation results, and designed a method to map the integrated coastal ecological risk. The five typical ecological receptors exhibited different risk responses when faced with different disturbances. Among the natural risks, invasive species and seawater pollution had the largest differences in risk values. Of the anthropogenic risks, the largest differences in risk values were for reclamation sea farming and industry. The results of the habitat risk assessment model, which were based on the exposure–consequence framework, and those of the hazard–vulnerability–damage model showed a strong positive correlation. Furthermore, the T-test results were tested with P values of less than 0.05, and were not significantly different at the 95% confidence level; thus, the results were valid. The variability in the evaluation results of different ecological receptors was used to map the integrated ecological risk. High- and very-high-risk areas were distributed along the coastline and the edge of the radiating sand ridge, and moderate-risk areas were distributed within the high-risk mudflat areas. The ecological risk values were quantified for each shore section; very-high-risk areas were mainly distributed in the central and estuaries shore sections, and moderate-risk areas were mainly distributed in the southern shoreline sections. The exposure and consequence values calculated using InVEST-HRA model were used to select ten primary factors represented by coastal erosion and sewage discharge as well as five secondary factors represented by green tides and coastal engineering. Finally, we suggested future ecological protection and risk prevention measures based on the shore sections and primary and secondary factors. This study takes the causal relationship of the risk occurrence process as a guideline, and combines the reflected difference characteristics among the risk elements to provide scientific support for the practice of ecological risk prevention and governance in the coastal zone.

Identification of geomorphic signatures of active tectonics in the Wadi Hagul Basin, Northwest Gulf of Suez, Egypt: insights from SRTM derived geomorphic indices and watershed analysis

The satellite data, include Landsat 7 Enhanced Thematic Mapper (ETM+), Landsat-8 (OLI), Shuttle Radar Topography Mission (SRTM), were processed and interpreted for creating an integrated geospatial map of the geomorphic indices (GI) of Wadi Hagul Basin (WHB). The present study is considered new, useful, and valuable in northwest of Gulf of Suez region to evaluate active tectonics using SRTM resulting drainage network and GI. Where fairly slight studies on active tectonics recognized on GI were prepared in the investigated area. The GI includes; Ratio of valley floor width to valley height (Vf), Transverse topographic symmetry factor (T), Stream length-gradient index (SL), Drainage basin shape (Bs), hypsometric integral (Hi), geomorphological landform and watershed analysis. Also, GI were integrated and combined with the seismic and structural lineaments intensities maps to create an integrated single index map. The morphotectonic geo-spatial distribution hazards map reveals that the high risky area is 82.75% of the total area, the moderate-risk area is 16.51%, and the low risky morph-tectonic area is 0.81%. Based on this map, it is necessary for planners and decision makers to extremely study attractive applicable action concerning the recommended mitigation measurements from this study to avoid any serious future problems in WHB and its surroundings areas.

Landscape Ecological Risk Assessment for the Tarim River Basin on the Basis of Land-Use Change

Land-use variation indicates the spatial differentiation of regional ecological risk. Landscape ecological risk assessment (LERA) has been used for the measurement and prediction of environmental quality. In the present study, the land-use dynamics of the Tarim River Basin from 2000 to 2020 were quantitatively analyzed using ENVI 5.6 software based on Landsat TM and ETM+ images (2000, 2010, and 2020). Moreover, the ecological risk level and its spatiotemporal differentiation features were explored using geostatistical methods based on landscape pattern indices. The results show that: (1) From 2000 to 2020, the arable land area increased the most (12,130.272 km2), and the woodland, wetland, water bodies, and building-land areas increased by 2416.541 km2, 4103.789 km2, 3331.230 km2, and 2330.860 km2, respectively. However, the bare-land area decreased the most (18,933.943 km2). (2) From 2000 to 2020, a decrease was detected in the landscape ecological risk index (LERI) of the basin, and the very low-, low-, and moderate-risk areas had the largest decrease. In addition, the area of the low- and moderate-risk areas gradually increased, while that of the high-risk areas was reduced. (3) The conversion rate of low-risk areas to very low-risk areas was the largest (5144.0907 km2/a), followed by that of high-risk areas to moderate-risk areas (4994.4765 km2/a). Therefore, the overall landscape ecological risk (LER) of the basin was reduced from 2000 to 2020, but the ecological risk of some areas, especially that of the glaciers and permanent snow-covered areas, still needs close attention.

Risk Assessment and Mapping of Flash Flood Vulnerable Zones in Arid Region, Fujairah City, UAE-Using Remote Sensing and GIS-Based Analysis

A flash flood is the most common natural hazard that endangers people’s lives, the economy, and infrastructure. Watershed management and planning are essential for reducing flood damages, particularly in residential areas, and mapping flash flood-sensitive zones. Flash flooding is an interface dynamic between geoterrain system factors such as geology, geomorphology, soil, drainage density, slope, and flood, rather than only water movement from higher to lower elevation. Consequently, the vulnerability to flash floods necessitates an awareness of and mapping topographical features. A flash flood vulnerable zones (FFVZ) map is essential for thorough flash flood risk assessment and management to minimize its detrimental effects, particularly in residential areas, especially in cities like Fujairah with seven wadis flowing into the city and even though it has two main dams and fifteen breaker dams. So, in this work, eight satellite image-derived parameters rainfall, elevation, slope, land use/land cover (LULC), drainage density, geology, geomorphology, and soil were combined to predict the flash flood-vulnerable zones using a weighted overlay technique based on geographic information systems (GIS). Each element of the thematic maps is ranked and weighted according to how vulnerable it is to flash floods in the study area, with 55 km2 being classified as a very highly vulnerable area, 78 km2 as a high-risk area, 9.3 km2 as a moderate risk area, 70 km2 as a low vulnerable area, and 257 km2 as a very low vulnerable area. In addition, places with a very high vulnerability level include the Fujairah Airport, Fujairah Port, some residential neighborhoods in the city’s center, oil storage areas, two hospitals, and universities. Additionally, from 1990 to the present, Landsat and Sentinel 2 data showed consistent changes in vegetation and built-up areas. Therefore, in addition to helping policy and decision-makers make the best choices about the efficacy of the study area’s protective structures against the risk of flash floods in the future, the results can also be a valuable source of information.

Aeolian disaster risk evaluation in the African Sahel

The Sahel is facing a serious environmental crisis due to aeolian disaster that has seriously affected the local development and survival of residents. Thus, evaluating the aeolian disaster risk levels and their variation in the Sahel is important. This study established an optimal model by evaluating the applicability of different models in the aeolian disaster risk determination in the Sahel. Using this model, the spatiotemporal changes in the risk subsystem of aeolian disaster (hazard, sensitivity, vulnerability, and restorability) and the aeolian disaster risk in the Sahel from 2000 to 2020 were analyzed. Based on this analysis, the impact of climate change and human activities on the aeolian disaster risk in the Sahel was evaluated. Results revealed that the variable fuzzy recognition (VFR) based on the aeolian disaster risk index (ADRI) model had the highest accuracy, reaching 89.72 %. The middle of the Sahel, located in the desert-grassland transition zone, exhibited a high hazard, sensitivity, and vulnerability, rendering it highly susceptible to aeolian disaster. The proportion of areas with very low and very high aeolian disaster risk levels decreased from 2000 to 2020, while those with low and high levels increased, and the change in moderate risk level areas remained relatively stable. Areas of low, moderate, and high risk are more sensitive to climate change and human activities and are subjected to greater pressure for change. Human activities were the main factor for the change of ADRI in the Sahel, accounting for 69.74 and 58.19 % of the increased and decreased areas of ADRI, respectively. This study evaluated the level of aeolian disaster risk in the Sahel and identified the main driving factors, providing a reference for Sahel countries to better implement the Green Great Wall (GGW) program in Africa, thereby mitigating the adverse effects of aeolian disaster.

Using information provision and interactive risk maps to motivate testing for radon

Radon exposure in homes is a leading cause of lung cancer, but the rate at which householders test for this environmental risk is low. In a pre-registered experiment with a nationally representative sample of adults (N = 1700), we used psychological theory to design information interventions to influence perceptions of risk from radon and motivate testing. Results show that information about radon increased belief that exposure would lead to negative consequences, but not perceived likelihood of exposure. Interactive maps depicting the geographical distribution of radon risk increased perceived likelihood of exposure, general worry and willingness to test for radon, but the effects depended on the map's attributes. Maps communicating risk using numeric frequencies of the number of homes in an area likely to be affected by radon (e.g., 1 in 5 homes) were more effective than ones that used simple statements (e.g., your home is at high risk). Adding an intermediate “moderate” risk category increased perceived risk compared to a binary high/low classification system among those in the moderate risk area, without altering perceptions of those at high risk. Other map features (colour and search functionality) had little impact. The best performing map led to 72% more people being willing to test for radon, compared to the map in use by the national Environmental Protection Agency at the time of the study. The results have implications for theories of risk perception and show the potential for techniques from psychological science to help mitigate a real-world environmental risk.

Source Analysis and Ecological Risk Assessment of Heavy Metals in the Arable Soil at the Geological High Background, Based on the Township Scale

Due to the extensive development of carbonate rocks in southwest China, heavy metals are naturally occurring elements that have high natural background levels in the environment. Therefore, it is important to conduct ecological risk assessments and identify potential sources of heavy metals in the geological high background area. Based on the township scale, a total of 307 surface soil samples were collected in Qinglong Town, Fengjie County, Chongqing. The concentrations of As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn and pH were analyzed and determined. The spatial distribution and source of soil heavy metals were discussed using the geostatistical analysis and an absolute principal component score-multiple linear regression (APCS-MLR) model in the studied area. The results showed that the average values of seven heavy metals (As, Cd, Cr, Cu, Hg, Ni, and Zn) in the arable soil exceeded the background values of Chongqing, and the cumulative effect of Cd and As was obvious. The concentrations of Cd significantly exceededthe screening values in The Risk Control Standard for Soil Environmental Quality and Soil Pollution in Agricultural Land (GB 15618-2018), with the over-standard rates of 52.12%. The spatial characteristics of soil heavy metal contents exhibited a pattern of high in the south and low in the north. PCA and APCS-MLR modeling revealed that the contributions of natural sources to Cr, Cu, Ni, and Zn were 86.62%, 64.34%, 76.44%, and 85.46%, respectively. As, Pb, and Hg mainly derived from industrial activities, which accounted for 74.63%, 61.90%, and 73.49%, respectively, and Cd was affected by both natural sources and industrial activities (accounting for 47.74% and 39.56% of the total Cd content, respectively). The evaluation of the soil by the Nemerow comprehensive index (P) showed that Cd pollution was relatively serious, accounting for 27.04% of soil pollution. The potential ecological hazard index showed that Cd and Hg were the main ecological hazard elements, and the distribution range of RI was 51.77 to 2228, indicating mainly mild and moderate risks, and the moderate and above risk areas in the study area were mainly located around the southern industrial source area. Altogether, our results revealed that in the study area, the heavy metal pollution was mainly caused by industrial activities, and the heavy metal pollution caused by geological background was mainly light to moderate. In conclusion, the medium and above risk areas in the study area were mainly caused by mineral and industrial activities, whereas the heavy metal pollution caused by geological background was mainly light to moderate pollution.

Community Compliance in Implementing Health Protocols in Polma Village, Kulawi District, Sigi Regency

The pandemic of Covid-19 has become a global problem and a threat to general public health. Sigi Regency is a moderate risk area with an incidence rate of 3,567 confirmed positive patients and a death rate reaching 111 people; Covid data in Kulawi District shows 171 people, particularly in Polma Village, reached 16 people. This research aimed to determine the description of community compliance in the application of health protocols in Polma Village, Kulawi District, Sigi Regency. This was qualitative research with a case study approach. Community compliance with mask-wearing is lacking due to feelings of claustrophobia, forgetfulness, neglect, and limited availability. Handwashing compliance is also low due to limited facilities and people ignoring them. Compliance with social distancing is lacking because it's not a familiar habit and is sometimes neglected. Compliance with avoiding crowds is lacking due to cultural obligations. However, compliance with reduced mobility has improved due to essential needs and compliance with health protocols

Spatial analysis of malaria hotspots in Dilla sub-watershed: Western Ethiopia.

This study aimed to examine the spatial variations in malaria hotspots along Dilla sub-watershed in western Ethiopia based on environmental factors for the prevalence; and compare the risk level along with districts and their respective kebele. The purpose was to identify the extent of the community's exposure to the risk of malaria due to their geographical and biophysical situations, and the results contribute to proactive interventions to halt the impacts. The descriptive survey design was used in this study. Ethiopia Central Statistical Agency based meteorological data, digital elevation model, and soil and hydrological data were integrated with other primary data such as the observations of the study area for ground truthing. The spatial analysis tools and software were used for watershed delineation, generating malaria risk map for all variables, reclassification of factors, weighted overlay analysis, and generation of risk maps. The findings of the study reveal that the significant spatial variations in magnitudes of malaria risk have persisted in the watershed due to discrepancy in their geographical and biophysical situations. Accordingly, significant areas in most of the districts in the watershed are characterized by high and moderate in malaria risks. In general, out of the total area of the watershed which accounts 2773 km2, about 54.8% (1522km2) identified as high and moderate malaria risk area. These areas are explicitly identified and mapped along with the districts and kebele in the watershed to make the result suitable for planning proactive interventions and other decision making. The research output may help the government and humanitarian organizations to prioritize the interventions based on identified spatial situations in severity of malaria risks. The study was aimed only for hotspot analysis which may not provide inclusive account for community's vulnerability to malaria. Thus, the findings in this study needs to be integrated with the socio-economic and other relevant data for better malaria management in the area. Therefore, future research should comprehend the analysis of vulnerability to the impacts of malaria through integrating the level of exposure to the risk, for instance identified in this study, with factors of sensitivity and adaptation capacity of the local community.