[Health protection and climate change require ambitious limit values for air pollutants in Europe : Opinion on the revision of the Directive on Air Quality and Clean Air for Europe of the Environmental Public Health commission of the Robert Koch Institute and the Federal Environment Agency]. / Gesundheitsschutz und Klimawandel erfordern ambitionierte Grenzwerte für Luftschadstoffe in Europa : Stellungnahme zur Revision der Richtlinie über Luftqualität und saubere Luft für Europa der Kommission Environmental Public Health des Robert Koch-Instituts und des Umweltbundesamtes.

Based on scientific findings, the World Health Organization (WHO) has recommended stricter guideline values for air quality in 2021. Significant reductions in the annual mean values of particulate matter (particle size 2.5 µm or smaller, PM2.5) and long-term exposure to nitrogen dioxide (NO2) and ozone (O3) were put forward. The risk of mortality already increases above the WHO guideline values, as shown in studies investigating low concentrations of air pollutants. In Germany, the 2021 WHO guideline values for PM2.5 and NO2 were clearly exceeded in 2022.In this position paper we give the following recommendations for the European Air Quality Directive: (1) set binding limit values according to WHO 2021, (2) apply the limit values to the whole of Europe, (3) continue and expand the established country-based monitoring networks, (4) expand air quality measurements for ultrafine particles and soot particles, and (5) link air pollution control and climate protection measures.Stricter limits for air pollutants require societal and political changes in areas such as mobility, energy use and generation, and urban and spatial planning. Implementation according to WHO 2021 would lead to a net economic benefit of 38 billion euros per year.Ambitious limit values for air pollutants also have an impact on climate change mitigation and its health impacts. The Environmental Public Health commission concludes that more ambitious limit values are crucial to enable effective health protection in Germany and calls for air pollutant limit values in line with the 2021 WHO recommendations to become binding in Europe.

QADI as a New Method and Alternative to Kappa for Accuracy Assessment of Remote Sensing-Based Image Classification.

Classification is a very common image processing task. The accuracy of the classified map is typically assessed through a comparison with real-world situations or with available reference data to estimate the reliability of the classification results. Common accuracy assessment approaches are based on an error matrix and provide a measure for the overall accuracy. A frequently used index is the Kappa index. As the Kappa index has increasingly been criticized, various alternative measures have been investigated with minimal success in practice. In this article, we introduce a novel index that overcomes the limitations. Unlike Kappa, it is not sensitive to asymmetric distributions. The quantity and allocation disagreement index (QADI) index computes the degree of disagreement between the classification results and reference maps by counting wrongly labeled pixels as A and quantifying the difference in the pixel count for each class between the classified map and reference data as Q. These values are then used to determine a quantitative QADI index value, which indicates the value of disagreement and difference between a classification result and training data. It can also be used to generate a graph that indicates the degree to which each factor contributes to the disagreement. The efficiency of Kappa and QADI were compared in six use cases. The results indicate that the QADI index generates more reliable classification accuracy assessments than the traditional Kappa can do. We also developed a toolbox in a GIS software environment.

Solar photovoltaic module detection using laboratory and airborne imaging spectroscopy data.

Over the past decades, solar panels have been widely used to harvest solar energy owing to the decreased cost of silicon-based photovoltaic (PV) modules, and therefore it is essential to remotely map and monitor the presence of solar PV modules. Many studies have explored on PV module detection based on color aerial photography and manual photo interpretation. Imaging spectroscopy data are capable of providing detailed spectral information to identify the spectral features of PV, and thus potentially become a promising resource for automated and operational PV detection. However, PV detection with imaging spectroscopy data must cope with the vast spectral diversity of surface materials, which is commonly divided into spectral intra-class variability and inter-class similarity. We have developed an approach to detect PV modules based on their physical absorption and reflection characteristics using airborne imaging spectroscopy data. A large database was implemented for training and validating the approach, including spectra-goniometric measurements of PV modules and other materials, a HyMap image spectral library containing 31 materials with 5627 spectra, and HySpex imaging spectroscopy data sets covering Oldenburg, Germany. By normalizing the widely used Hydrocarbon Index (HI), we solved the intra-class variability caused by different detection angles, and validated it against the spectra-goniometric measurements. Knowing that PV modules are composed of materials with different transparencies, we used a group of spectral indices and investigated their interdependencies for PV detection with implementing the image spectral library. Finally, six well-trained spectral indices were applied to HySpex data acquired in Oldenburg, Germany, yielding an overall PV map. Four subsets were selected for validation and achieved overall accuracies, producer's accuracies and user's accuracies, respectively. This physics-based approach was validated against a large database collected from multiple platforms (laboratory measurements, airborne imaging spectroscopy data), thus providing a robust, transferable and applicable way to detect PV modules using imaging spectroscopy data. We aim to create greater awareness of the potential importance and applicability of airborne and spaceborne imaging spectroscopy data for PV modules identification.

[Nighttime transportation noise annoyance in Germany: personal and regional differences in the German National Cohort Study]. / Nächtliche Verkehrslärmbelästigung in Deutschland: individuelle und regionale Unterschiede in der NAKO Gesundheitsstudie.

BACKGROUND: Noise annoyance is associated with adverse health-related conditions and reduced wellbeing. Thereby, subjective noise annoyance depends on the objective noise exposure and is modified by personal and regional factors. OBJECTIVE: How many participants of the German National Cohort Study (GNC; NAKO Gesundheitsstudie) were annoyed by transportation noise during nighttime and what factors were associated with noise annoyance? MATERIALS AND METHODS: This cross-sectional analysis included 86,080 participants from 18 study centers, examined from 2014 to 2017. We used multinomial logistic regression to investigate associations of personal and regional factors to noise annoyance (slightly/moderately or strongly/extremely annoyed vs. not annoyed) mutually adjusting for all factors in the model. RESULTS: Two thirds of participants were not annoyed by transportation noise during nighttime and one in ten reported strong/extreme annoyance with highest percentages for the study centers Berlin-Mitte and Leipzig. The strongest associations were seen for factors related to the individual housing situation like the bedroom being positioned towards a major road (OR of being slightly/moderately annoyed: 4.26 [95% CI: 4.01;4.52]; OR of being strongly/extremely annoyed: 13.36 [95% CI: 12.47;14.32]) compared to a garden/inner courtyard. Participants aged 40-60 years and those in low- and medium-income groups reported greater noise annoyance compared to younger or older ones and those in the high-income group. CONCLUSION: In this study from Germany, transportation noise annoyance during nighttime varied by personal and regional factors.

Modeling the spatially varying risk factors of dengue fever in Jhapa district, Nepal, using the semi-parametric geographically weighted regression model.

Dengue fever is expanding rapidly in many tropical and subtropical countries since the last few decades. However, due to limited research, little is known about the spatial patterns and associated risk factors on a local scale particularly in the newly emerged areas. In this study, we explored spatial patterns and evaluated associated potential environmental and socioeconomic risk factors in the distribution of dengue fever incidence in Jhapa district, Nepal. Global and local Moran's I were used to assess global and local clustering patterns of the disease. The ordinary least square (OLS), geographically weighted regression (GWR), and semi-parametric geographically weighted regression (s-GWR) models were compared to describe spatial relationship of potential environmental and socioeconomic risk factors with dengue incidence. Our result revealed heterogeneous and highly clustered distribution of dengue incidence in Jhapa district during the study period. The s-GWR model best explained the spatial association of potential risk factors with dengue incidence and was used to produce the predictive map. The statistical relationship between dengue incidence and proportion of urban area, proximity to road, and population density varied significantly among the wards while the associations of land surface temperature (LST) and normalized difference vegetation index (NDVI) remained constant spatially showing importance of mixed geographical modeling approach (s-GWR) in the spatial distribution of dengue fever. This finding could be used in the formulation and execution of evidence-based dengue control and management program to allocate scare resources locally.

[A spatially explicit analysis of traffic accidents involving pedestrians and cyclists in Berlin]. / Eine räumlich differenzierte Analyse von Verkehrsunfällen mit Radfahrer- und Fußgängerbeteiligung in Berlin.

BACKGROUND: In many German cities and counties, sustainable mobility concepts that strengthen pedestrian and cyclist traffic are promoted. From the perspectives of urban development, traffic planning and public healthcare, a spatially differentiated analysis of traffic accident data is decisive. OBJECTIVES: 1) The identification of spatial and temporal patterns of the distribution of accidents involving cyclists and pedestrians, 2) the identification of hotspots and exploration of possible underlying causes and 3) the critical discussion of benefits and challenges of the results and the derivation of conclusions. MATERIAL AND METHODS: Spatio-temporal distributions of data from accident statistics in Berlin involving pedestrians and cyclists from 2011 to 2015 were analysed with geographic information systems (GIS). RESULTS: While the total number of accidents remains relatively stable for pedestrian and cyclist accidents, the spatial distribution analysis shows, however, that there are significant spatial clusters (hotspots) of traffic accidents with a strong concentration in the inner city area. CONCLUSIONS: In a critical discussion, the benefits of geographic concepts are identified, such as spatially explicit health data (in this case traffic accident data), the importance of the integration of other data sources for the evaluation of the health impact of areas (traffic accident statistics of the police), and the possibilities and limitations of spatial-temporal data analysis (spatial point-density analyses) for the derivation of decision-supported recommendations and for the evaluation of policy measures of health prevention and of health-relevant urban development.

Childhood overweight in Berlin: intra-urban differences and underlying influencing factors.

BACKGROUND: In recent years, childhood overweight and obesity have become an increasing and challenging phenomenon in Western cities. A lot of studies have focused on the analysis of factors such as individual dispositions and nutrition balances, among others. However, little is known about the intra-urban spatial patterns of childhood overweight and its associations with influencing factors that stretch from an individual to a neighbourhood level. The aim of this paper is to analyse the spatial patterns of childhood obesity in Berlin, and also to explore and test for associations with a complex set of risk factors at the individual, household and neighbourhood levels. METHODS: We use data from a survey of 5-6 year-olds that includes health status, height, and weight, as well as several socioeconomic and other risk variables. In addition, we use a set of neighbourhood variables, such as distance, and density measures of parks or fast food restaurants. Our outcome variable is the percentage of children of 5-6 years who were reported overweight or obese in 2012. The aggregated data is available for 60 areas in Berlin. We first analyse the outcome and risk factor data descriptively, and subsequently apply a set of regression analyses to test for associations between reported overweight and obesity, and also individual, household and neighbourhood characteristics. RESULTS: Our analysis returned a distinct spatial distribution of childhood overweight in Berlin with highest shares in the city centre. Moreover, we were able to identify significant effects regarding the social index, and the percentage of non-German children being obese or overweight; additionally, we identified fast food restaurant density as a possible influencing factor. For the other variables, including the neighbourhood variables, we could not identify a significant association on this aggregated level of analysis. CONCLUSIONS: Our findings confirm the results of earlier studies, in which the social status and percentage of non-German children is very important in terms of the association with childhood overweight and obesity. Unlike many studies conducted in North America, this study did not reveal an influence of neighbourhood variables. We argue that European urban structures differ from North American structures and highlight the need for a more detailed analysis of the association between the neighbourhood environment and the physical activity of children in urban setting.

Spatiotemporal analysis of dengue fever in Nepal from 2010 to 2014.

BACKGROUND: Due to recent emergence, dengue is becoming one of the major public health problems in Nepal. The numbers of reported dengue cases in general and the area with reported dengue cases are both continuously increasing in recent years. However, spatiotemporal patterns and clusters of dengue have not been investigated yet. This study aims to fill this gap by analyzing spatiotemporal patterns based on monthly surveillance data aggregated at district. METHODS: Dengue cases from 2010 to 2014 at district level were collected from the Nepal government's health and mapping agencies respectively. GeoDa software was used to map crude incidence, excess hazard and spatially smoothed incidence. Cluster analysis was performed in SaTScan software to explore spatiotemporal clusters of dengue during the above-mentioned time period. RESULTS: Spatiotemporal distribution of dengue fever in Nepal from 2010 to 2014 was mapped at district level in terms of crude incidence, excess risk and spatially smoothed incidence. Results show that the distribution of dengue fever was not random but clustered in space and time. Chitwan district was identified as the most likely cluster and Jhapa district was the first secondary cluster in both spatial and spatiotemporal scan. July to September of 2010 was identified as a significant temporal cluster. CONCLUSION: This study assessed and mapped for the first time the spatiotemporal pattern of dengue fever in Nepal. Two districts namely Chitwan and Jhapa were found highly affected by dengue fever. The current study also demonstrated the importance of geospatial approach in epidemiological research. The initial result on dengue patterns and risk of this study may assist institutions and policy makers to develop better preventive strategies.

Health effects of shrinking hyper-saline lakes: spatiotemporal modeling of the Lake Urmia drought on the local population, case study of the Shabestar County.

Climate change and its respective environmental impacts, such as dying lakes, is widely acknowledged. Studies on the impact of shrinking hyper-saline lakes suggest severe negative consequences for the health of the affected population. The primary aim was to investigate the relationship between changes in the water level of the hyper-saline Lake Urmia, along with the associated salt release, and the prevalence of hypertension and the general state of health of the local population in Shabestar County north of the lake. Moreover, we sought to map the vulnerability of the local population to the health risks associated with salt-dust scatter using multiple environmental and demographic characteristics. We applied a spatiotemporal analysis of the environmental parameters of Lake Urmia and the health of the local population. We analyzed health survey data from local health care centers and a national STEPS study in Shabestar County, Iran. We used a time-series of remote sensing images to monitor the trend of occurrence and extent of salt-dust storms between 2012 and 2020. To evaluate the impacts of lake drought on the health of the residences, we investigated the spatiotemporal correlation of the lake drought and the state of health of local residents. We applied a GIScience multiple decision analysis to identify areas affected by salt-dust particles and related these to the health status of the residents. According to our results, the lake drought has significantly contributed to the increasing cases of hypertension in local patients. The number of hypertensive patients has increased from 2.09% in 2012 to 19.5% in 2019 before decreasing slightly to 16.05% in 2020. Detailed results showed that adults, and particularly females, were affected most by the effects of the salt-dust scatter in the residential areas close to the lake. The results of this study provide critical insights into the environmental impacts of the Lake Urmia drought on the human health of the residents. Based on the results we suggest that detailed socioeconomic studies might be required for a comprehensive analysis of the human health issues in this area. Nonetheless, the proposed methods can be applied to monitor the environmental impacts of climate change on human health.

Exploration of the COVID-19 pandemic at the neighborhood level in an intra-urban setting.

The COVID-19 pandemic represents a worldwide threat to health. Since its onset in 2019, the pandemic has proceeded in different phases, which have been shaped by a complex set of influencing factors, including public health and social measures, the emergence of new virus variants, and seasonality. Understanding the development of COVID-19 incidence and its spatiotemporal patterns at a neighborhood level is crucial for local health authorities to identify high-risk areas and develop tailored mitigation strategies. However, analyses at the neighborhood level are scarce and mostly limited to specific phases of the pandemic. The aim of this study was to explore the development of COVID-19 incidence and spatiotemporal patterns of incidence at a neighborhood scale in an intra-urban setting over several pandemic phases (March 2020-December 2021). We used reported COVID-19 case data from the health department of the district Berlin-Neukölln, Germany, additional socio-demographic data, and text documents and materials on implemented public health and social measures. We examined incidence over time in the context of the measures and other influencing factors, with a particular focus on age groups. We used incidence maps and spatial scan statistics to reveal changing spatiotemporal patterns. Our results show that several factors may have influenced the development of COVID-19 incidence. In particular, the far-reaching measures for contact reduction showed a substantial impact on incidence in Neukölln. We observed several age group-specific effects: school closures had an effect on incidence in the younger population (< 18 years), whereas the start of the vaccination campaign had an impact primarily on incidence among the elderly (> 65 years). The spatial analysis revealed that high-risk areas were heterogeneously distributed across the district. The location of high-risk areas also changed across the pandemic phases. In this study, existing intra-urban studies were supplemented by our investigation of the course of the pandemic and the underlying processes at a small scale over a long period of time. Our findings provide new insights for public health authorities, community planners, and policymakers about the spatiotemporal development of the COVID-19 pandemic at the neighborhood level. These insights are crucial for guiding decision-makers in implementing mitigation strategies.

Exploring the Spatial Relative Risk of COVID-19 in Berlin-Neukölln.

Identifying areas with high and low infection rates can provide important etiological clues. Usually, areas with high and low infection rates are identified by aggregating epidemiological data into geographical units, such as administrative areas. This assumes that the distribution of population numbers, infection rates, and resulting risks is constant across space. This assumption is, however, often false and is commonly known as the modifiable area unit problem. This article develops a spatial relative risk surface by using kernel density estimation to identify statistically significant areas of high risk by comparing the spatial distribution of address-level COVID-19 cases and the underlying population at risk in Berlin-Neukölln. Our findings show that there are varying areas of statistically significant high and low risk that straddle administrative boundaries. The findings of this exploratory analysis further highlight topics such as, e.g., Why were mostly affluent areas affected during the first wave? What lessons can be learned from areas with low infection rates? How important are built structures as drivers of COVID-19? How large is the effect of the socio-economic situation on COVID-19 infections? We conclude that it is of great importance to provide access to and analyse fine-resolution data to be able to understand the spread of the disease and address tailored health measures in urban settings.

Mapping urban malaria and diarrhea mortality in Accra, Ghana: evidence of vulnerabilities and implications for urban health policy.

Historic increase in urban population numbers in the face of shrinking urban economies and declining social services has meant that a large proportion of the urban population lives in precarious urban conditions, which provide the grounds for high urban health risks in low income countries. This study aims to identify, investigate, and contrast the spatial patterns of vulnerability and risk of two major causes of mortality, viz malaria and diarrhea mortalities, in order to optimize resource allocation for effective urban environmental management and improvement in urban health. A spatial cluster analysis of the observed urban malaria and diarrhea mortalities for the whole city of Accra was conducted. We obtained routinely reported mortality data for the period 1998-2002 from the Ghana Vital Registration System (VRS), computed the fraction of deaths due to malaria and diarrhea at the census cluster level, and analyzed and visualized the data with Geographic Information System (GIS, ArcMap 9.3.1). Regions of identified hotspots, cold spots, and excess mortalities were observed to be associated with some socioeconomic and neighborhood urban environmental conditions, suggesting uneven distribution of risk factors for both urban malaria and diarrhea in areas of rapid urban transformation. Case-control and/or longitudinal studies seeking to understand the individual level factors which mediate socioenvironmental conditions in explaining the observed excess urban mortalities and to establish the full range of risk factors might benefit from initial vulnerability mapping and excess risk analysis using geostatistical approaches. This is key to evidence-based urban health policy reforms in rapidly urbanizing areas in low income economies.

Mental health in the slums of Dhaka - a geoepidemiological study.

BACKGROUND: Urban health is of global concern because the majority of the world's population lives in urban areas. Although mental health problems (e.g. depression) in developing countries are highly prevalent, such issues are not yet adequately addressed in the rapidly urbanising megacities of these countries, where a growing number of residents live in slums. Little is known about the spectrum of mental well-being in urban slums and only poor knowledge exists on health promotive socio-physical environments in these areas. Using a geo-epidemiological approach, the present study identified factors that contribute to the mental well-being in the slums of Dhaka, which currently accommodates an estimated population of more than 14 million, including 3.4 million slum dwellers. METHODS: The baseline data of a cohort study conducted in early 2009 in nine slums of Dhaka were used. Data were collected from 1,938 adults (≥ 15 years). All respondents were geographically marked based on their households using global positioning systems (GPS). Very high-resolution land cover information was processed in a Geographic Information System (GIS) to obtain additional exposure information. We used a factor analysis to reduce the socio-physical explanatory variables to a fewer set of uncorrelated linear combinations of variables. We then regressed these factors on the WHO-5 Well-being Index that was used as a proxy for self-rated mental well-being. RESULTS: Mental well-being was significantly associated with various factors such as selected features of the natural environment, flood risk, sanitation, housing quality, sufficiency and durability. We further identified associations with population density, job satisfaction, and income generation while controlling for individual factors such as age, gender, and diseases. CONCLUSIONS: Factors determining mental well-being were related to the socio-physical environment and individual level characteristics. Given that mental well-being is associated with physiological well-being, our study may provide crucial information for developing better health care and disease prevention programmes in slums of Dhaka and other comparable settings.

Exploring Environmental Health Inequalities: A Scientometric Analysis of Global Research Trends (1970-2020).

Environmental health inequalities (EHI), understood as differences in environmental health factors and in health outcomes caused by environmental conditions, are studied by a wide range of disciplines. This results in challenges to both synthesizing key knowledge domains of the field. This study aims to uncover the global research status and trends in EHI research, and to derive a conceptual framework for the underlying mechanisms of EHI. In total, 12,320 EHI publications were compiled from the Web of Science core collection from 1970 to 2020. Scientometric analysis was adopted to characterize the research activity, distribution, focus, and trends. Content analysis was conducted for the highlight work identified from network analysis. Keyword co-occurrence and cluster analysis were applied to identify the knowledge domain and develop the EHI framework. The results show that there has been a steady increase in numbers of EHI publications, active journals, and involved disciplines, countries, and institutions since the 2000s, with marked differences between countries in the number of published articles and active institutions. In the recent decade, environment-related disciplines have gained importance in addition to social and health sciences. This study proposes a framework to conceptualize the multi-facetted issues in EHI research referring to existing key concepts.

Predicting traffic noise using land-use regression-a scalable approach.

BACKGROUND: In modern societies, noise is ubiquitous. It is an annoyance and can have a negative impact on human health as well as on the environment. Despite increasing evidence of its negative impacts, spatial knowledge about noise distribution remains limited. Up to now, noise mapping is frequently inhibited by the necessary resources and therefore limited to selected areas. OBJECTIVE: Based on the assumption, that prevalent noise is determined by the arrangement of sources and the surrounding environment in which the sound propagates, we build a geostatistical model representing these parameters. Aiming for a large-scale noise mapping approach, we utilize publicly available data, context-aware feature engineering and a linear land-use regression (LUR) model. METHODS: Compliant to the European Noise Directive 2002/49/EG, we work at a high spatial granularity of 10 × 10-m resolution. As reference, we use the day-evening-night noise level indicator Lden. Therewith, we carry out 2000 virtual field campaigns simulating different sampling schemes and introduce spatial cross-validation concepts to test the transferability to new areas. RESULTS: The experimental results suggest the necessity for more than 500 samples stratified over the different noise levels to produce a representative model. Eventually, using 21 selected variables, our model was able to explain large proportions of the yearly averaged road noise (Lden) variability (R2 = 0.702) with a mean absolute error of 4.24 dB(A), 3.84 dB(A) for build-up areas, respectively. In applying this best performing model for an area-wide prediction, we spatially close the blank spots in existing noise maps with continuous noise levels for the entire range from 24 to 106 dB(A). SIGNIFICANCE: This data is new, particular for small communities that have not been mapped sufficiently in Europe so far. In conjunction, our findings also supplement conventionally sampled studies using physical microphones and spatially blocked cross-validations.

Scenario-based analysis of the impacts of lake drying on food production in the Lake Urmia Basin of Northern Iran.

In many parts of the world, lake drying is caused by water management failures, while the phenomenon is exacerbated by climate change. Lake Urmia in Northern Iran is drying up at such an alarming rate that it is considered to be a dying lake, which has dire consequences for the whole region. While salinization caused by a dying lake is well understood and known to influence the local and regional food production, other potential impacts by dying lakes are as yet unknown. The food production in the Urmia region is predominantly regional and relies on local water sources. To explore the current and projected impacts of the dying lake on food production, we investigated changes in the climatic conditions, land use, and land degradation for the period 1990-2020. We examined the environmental impacts of lake drought on food production using an integrated scenario-based geoinformation framework. The results show that the lake drought has significantly affected and reduced food production over the past three decades. Based on a combination of cellular automaton and Markov modeling, we project the food production for the next 30 years and predict it will reduce further. The results of this study emphasize the critical environmental impacts of the Urmia Lake drought on food production in the region. We hope that the results will encourage authorities and environmental planners to counteract these issues and take steps to support food production. As our proposed integrated geoinformation approach considers both the extensive impacts of global climate change and the factors associated with dying lakes, we consider it to be suitable to investigate the relationships between environmental degradation and scenario-based food production in other regions with dying lakes around the world.

A spatial epidemiological analysis of self-rated mental health in the slums of Dhaka.

BACKGROUND: The deprived physical environments present in slums are well-known to have adverse health effects on their residents. However, little is known about the health effects of the social environments in slums. Moreover, neighbourhood quantitative spatial analyses of the mental health status of slum residents are still rare. The aim of this paper is to study self-rated mental health data in several slums of Dhaka, Bangladesh, by accounting for neighbourhood social and physical associations using spatial statistics. We hypothesised that mental health would show a significant spatial pattern in different population groups, and that the spatial patterns would relate to spatially-correlated health-determining factors (HDF). METHODS: We applied a spatial epidemiological approach, including non-spatial ANOVA/ANCOVA, as well as global and local univariate and bivariate Moran's I statistics. The WHO-5 Well-being Index was used as a measure of self-rated mental health. RESULTS: We found that poor mental health (WHO-5 scores < 13) among the adult population (age ≥15) was prevalent in all slum settlements. We detected spatially autocorrelated WHO-5 scores (i.e., spatial clusters of poor and good mental health among different population groups). Further, we detected spatial associations between mental health and housing quality, sanitation, income generation, environmental health knowledge, education, age, gender, flood non-affectedness, and selected properties of the natural environment. CONCLUSIONS: Spatial patterns of mental health were detected and could be partly explained by spatially correlated HDF. We thereby showed that the socio-physical neighbourhood was significantly associated with health status, i.e., mental health at one location was spatially dependent on the mental health and HDF prevalent at neighbouring locations. Furthermore, the spatial patterns point to severe health disparities both within and between the slums. In addition to examining health outcomes, the methodology used here is also applicable to residuals of regression models, such as helping to avoid violating the assumption of data independence that underlies many statistical approaches. We assume that similar spatial structures can be found in other studies focussing on neighbourhood effects on health, and therefore argue for a more widespread incorporation of spatial statistics in epidemiological studies.

A scenario-based approach for urban water management in the context of the COVID-19 pandemic and a case study for the Tabriz metropolitan area, Iran.

The world's poorest countries were hit hardest by COVID-19 due to their limited capacities to combat the pandemic. The urban water supply and water consumption are affected by the pandemic because it intensified the existing deficits in the urban water supply and sanitation services. In this study, we develop an integrated spatial analysis approach to investigate the impacts of COVID-19 on multi-dimensional Urban Water Consumption Patterns (UWCPs) with the aim of forecasting the water demand. We selected the Tabriz metropolitan area as a case study area and applied an integrated approach of GIS spatial analysis and regression-based autocorrelation assessment to develop the UWCPs for 2018, 2019 and 2020. We then employed GIS-based multi-criteria decision analysis and a CA-Markov model to analyze the water demand under the impacts of COVID-19 and to forecast the UWCPs for 2021, 2022 and 2023. In addition, we tested the spatial uncertainty of the prediction maps using the Dempster Shafer Theory. The results show that the domestic water consumption increased by 17.57% during the year 2020 as a result of the COVID-19 pandemic. The maximum increase in water consumption was observed in spring 2020 (April-June) when strict quarantine regulations were in place. Based on our results, the annual water deficit in Tabriz has increased from ~18% to about 30% in 2020. In addition, our projections show that this may further increase to about 40-45% in 2021. Relevant stakeholders can use the findings to develop evidence-informed strategies for sustainable water resource management in the post-COVID era. This research also makes other significant contributions. From the environmental perspective, since COVID-19 has affected resource management in many parts of the world, the proposed method can be applied to similar contexts to mitigate the adverse impacts and developed better informed recovery plans.

An automated deep learning convolutional neural network algorithm applied for soil salinity distribution mapping in Lake Urmia, Iran.

Traditional soil salinity studies are time-consuming and expensive, especially over large areas. This study proposed an innovative deep learning convolutional neural network (DL-CNN) data-driven approach for SSD mapping. Multi-spectral remote sensing data encompassing Landsat series images provide the possibility for frequent assessment of SSD in various regions of the world. Therefore, Landsat 7 ETM+ and 8 OLI images were acquired for years 2005, 2010, 2015 and 2019. Totally, 704 sample points collected from the top 20 cm of the soil surface, which 70% was used to train the network and the remains (30%) were utilized to validate the network. Accordingly, DL-CNN model trained using remote sensing (RS)-derived variables (land surface temperature (LST), Soil moisture (SM) and evapotranspiration) and geospatial data such as NDVI and landuse. To train the CNN, ReLu, Cross-entropy and ADAM were employed respectively as activation, loss/cost functions and optimizer. The results indicated the high confidence of OA 0.94.02, 0.93.99, 0.94.87 and 0.95.0 respectively for years 2005, 2010, 2015 and 2019. These accuracies demonstrated the best performance of automated DL-CNN for SSD mapping compared to RS soil salinity indexes. Furthermore, the FR and WOE models applied in order to generate a geospatial assessment of the DL-CNN classification results. According to the FR model, landuse, LST, LST and NDVI with the frequency ratio of 0.98.25, 0.94.03, 0.97.23 and 0.96.36 selected respectively as more effective factors for SSD in the study area for years 2005, 2010, 2015 and 2019. Also based on the WOE model, landuse, LST, landuse and NDVI with the WOE of 0.88.25, 0.91.88, 0.87.43 and 0.89.02 were ranked respectively for years 2005, 2010, 2015 and 2019 as efficient variables for SSD. In sum, our introduced method can be recommended for SDD spatial modelling in other favored areas with similar environmental conditions.

Modeling and mapping the burden of disease in Kenya.

Precision public health approaches are crucial for targeting health policies to regions most affected by disease. We present the first sub-national and spatially explicit burden of disease study in Africa. We used a cross-sectional study design and assessed data from the Kenya population and housing census of 2009 for calculating YLLs (years of life lost) due to premature mortality at the division level (N = 612). We conducted spatial autocorrelation analysis to identify spatial clusters of YLLs and applied boosted regression trees to find statistical associations between locational risk factors and YLLs. We found statistically significant spatial clusters of high numbers of YLLs at the division level in western, northwestern, and northeastern areas of Kenya. Ethnicity and household crowding were the most important and significant risk factors for YLL. Further positive and significantly associated variables were malaria endemicity, northern geographic location, and higher YLL in neighboring divisions. In contrast, higher rates of married people and more precipitation in a division were significantly associated with less YLL. We provide an evidence base and a transferable approach that can guide health policy and intervention in sub-national regions afflicted by disease burden in Kenya and other areas of comparable settings.