Geospatial Health is the Journal of the GIS Laboratory at the Department of Veterinary Medicine and Animal Production, Regional Center for Monitoring Parasitic Infections (CREMOPAR), University of Naples Federico II (https://www.mvpa-unina.org). Geospatial Health is also the official journal of the International Society of Geospatial Health (www.GnosisGIS.org). The journal was founded in 2006 at the University of Naples Federico II by Giuseppe Cringoli, John B. Malone, Robert Bergquist and Laura Rinaldi. The focus of the journal is on all aspects of the application of geographical information systems, remote sensing, global positioning systems, spatial statistics and other geospatial tools in human and veterinary health. The journal publishes two issues per year.
PAGEPress Scientific Publications, Pavia, Italy
Integration of accelerometers, global positioning systems (GPS) and geographical information systems (GIS) for measuring physical activity
We tested the feasibility of integrating Actigraph accelerometers (AG), Global Positioning Systems (GPS) and Geographical Information Systems (GIS) to explore the physical activity in 26 healthy adults and 7 post-stroke individuals. The study subjects wore AG and GPS devices for 7 days. Feasibility outcomes were participants’ experience of using these devices and data quality regarding i) valid and synchronized data between the AG and GPS; ii) GPS data distribution among participants living in areas characterized by differently developed built environments; and iii) time and intensity of physical activity in and outside the home. There were >10 hours of synchronized data between the devices and the majority (94%) of participants, irrespective of group, did not report any problems using the AG or GPS. Individuals living in low-density built environment had a higher percentage of GPS points closer to the home compared to those living in areas with high-density built environment where GPS scattering occurred. Although methodological challenges regarding scattering and GPS signal loss in densely built environment in urban areas, the results support the overall feasibility of integrating AG, GPS and GIS to investigate physical activity behaviour.
Copyright (c) 2025 the Author(s) https://creativecommons.org/licenses/by-nc/4.0
Evaluation of spatial cluster detection methods for dengue fever in the state of Paraiba, Brazil
This study is a quantitative, ecological, descriptive, retrospective, cross-sectional study on dengue in the state of Paraíba in north-eastern Brazil aimed to compare the performance of spatial clustering methods based on epidemiological data. The population consisted of all people residing in the state, and the sample was all dengue fever cases reported annually between 2018 and 2022. The residence localization of people suffering from dengue fever was used to identify the spatial distribution of this infection in the Paraíba State. Scan Statistics, Besag-Newell, Getis-Ord, MStatistics and Tango were used and it was observed that the methods Getis-Ord, M-Statistic and Tango showed large spatial clusters, which included municipalities with high and low values. Scan Statistics and Besag-Newell’s method also showed most of these clusters, with Scan Statistic providing better agreement with the high Standardized Incidence Ratio (SIR) than Besag-Newell’s method. In conclusion, Scan statistic outperformed the other methods by identifying significant clusters in greater proportion in all study periods when mapping using Rigorous Impact Evaluation (RIE) was applied. However, it is necessary to consider each method’s assumptions to select the most appropriate method for each application. Thus, this study provides relevant elements to help decision makers manage and prevent diseases, such as dengue fever and other vector-borne diseases.
Venomous snakebite risk and its implications in Zacatecas State, Mexico 2007-2017
Zacatecas is a Mexican state from where there are few studies about biodiversity, venomous ophidians and people’s experience of snakebites. In the state, there are 12 species of venomous snakes distributed in three genera: Crotalus, Micruroides and Micrurus, which could represent some risk for the locals. The objective of this study was to make use of Geographic Information Systems (GIS) and programming to determine the relationship between population variables and the number of snakebites registered by the Zacatecas Health Services (SSZ) from 2007 to 2017 at the municipal level. Climatic, social and biological variables were used to gain a better understanding of the situation. It was found that men working in livestock breeding, agriculture, subsistence hunting or mining are more vulnerable, especially if older than 65. The municipalities of Concepción del Oro, Villa de Cos, El Plateado de Joaquín Amaro, Loreto and Ojocaliente exhibit the highest risk, while special monitoring must be conducted in Guadalupe, Fresnillo and Zacatecas due to their high population density, as well as in Valparaíso on account of its rich venomous ophidian fauna. Additionally, it is suggested to carry out preventive actions and detailed data gathering about snakebites to guarantee information quality. This study constitutes the first formal, detailed work about the epidemiological panorama of envenoming caused by the bite of a snake (ophidiotoxicosis) in Zacatecas from which further investigation and modelling may derive.
Evaluation of emergency medical service application from a geographical location perspective in Turkey
An important area of use of the geographic information systems in health is the organization of Emergency Medical Services (EMS). In this study, the EMS application offered in Turkey’s 81 provinces, in particular, Istanbul metropolis, which has the highest population in the country, was examined with a statistical approach. It was determined that the correlation level between the number of EMS stations and the population of the 39 districts of Istanbul was higher compared to the land area and population density; the number of EMS stations in the Fatih District was significantly greater than the median value of the number of EMS stations in all districts of Istanbul. It was determined that the number of EMS stations, ambulances, and hospitals in Istanbul is significantly greater than the median value of all provinces in Turkey; the population density per hospital and EMS station in Istanbul is significantly greater than the median value of all provinces, and the area value is smaller than the median value of all provinces. Ambulance response time, hospital transfer time and reasons for delays at these stages were questioned through a survey. The most common reasons for delay were traffic congestion, followed by the few and far distances of ambulance stations. Considering the problems arising from the geographical location of EMS stations and hospitals, it is expected that taking population density into account when planning EMS station distribution would contribute to increased efficiency in EMS and equality in access to services.
Promoting sustainable health equity: accessibility analysis and optimization of tertiary hospital networks in China’s metropolitan areas
Healthcare accessibility is vital for sustainable urban development, ensuring timely diagnosis, chronic disease management, and emergency response. However, in many developing countries, the uneven distribution of advanced healthcare services exacerbates health disparities. Taking Tianjin in China as an example, this study aims to evaluate the spatial accessibility of tertiary hospitals and optimize hospital placement to improve healthcare coverage. Using the Gaussian Two-Step Floating Catchment Area (G2SFCA) method, the study integrated high-resolution spatial data on hospital locations, population density, and transportation networks, assessing the accessibility of higher-level healthcare services citywide. The results indicate that central urban districts exhibited high accessibility, where all demand points were within the 1-hour service range. In contrast, suburban districts had an average accessibility of 0.194, and outer suburban districts had the lowest citywide mean of 0.005, with less than 20% of the area covered. Despite its economic significance, Binhai New Area’s healthcare accessibility remained inadequate, with a mean score of 0.010. The application of a location-allocation model to optimize the placement of 24 planned new hospitals, prioritizing areas with high population density and low accessibility resulted in an increased population coverage from 73.31% to 95.05%, significantly reducing non-accessible points. This study aligns with the United Nations’ Sustainable Development Goals 3 and 11, advocating a hierarchical healthcare system, telemedicine, and improved transportation to minimize time costs and reduce inequities.
Urban gentrification and infectious diseases: an interdisciplinary narrative review
Urban gentrification, the transformation of neighbourhoods by influx of new residential groups, leading to displacement of lowerincome communities, is a complex, multifaceted process with significant but generally unexplored public health implications. This study focused on the impact of this process on infectious disease dynamics investigating key factors such as sociodemographic disparities, economic conditions, housing and urban environmental changes. A systemic literature research was performed based on the search terms: gentrification and infectious disease in PubMed, Scopus, Web of Science, ScienceDirect, and Google Scholar, with additional references identified using the snowballing method. After screening the resulting 542 articles, 14 studies were selected based on relevance, with data were extracted through a consensusdriven process. This review identified the complex challenges posed by gentrification in the context of infectious disease dynamics and burdens providing valuable insights both to academic discourse and public health policy discussions. Gentrification may contribute to higher infection rates within specific urban neighbourhoods or among certain residents. For blood-borne and Sexually Transmitted Infections (STIs), gentrification leads to reduced access to essential healthcare services, including HIV and STI testing, particularly among marginalised populations, such as female sex workers and LGBTQ+ communities. For airborne diseases, gentrification can exacerbate health inequalities by increasing residential overcrowding and displacement from gentrified areas to more disadvantaged suburbs. Housing and urban planning associated with changes in the urban environment are primarily linked with vector-borne diseases, tick-borne diseases in particular, among displaced populations. We advocate the use of spatial epidemiology to examine the potential impact of gentrification on the risk for infectious diseases. Since many gentrification metrics are area-specific, mapping and visualising key indicator data can pre-emptively support practical decision-making. This approach also helps capture the complex dynamics of displacement and the within-place changes experienced by populations affected by gentrification, which might affect infectious disease dynamics. Finally, we outline key research priorities to bridge existing knowledge gaps in future multidisciplinary research on infectious diseases and gentrification.
Spatial autocorrelation patterns and factors associated with regular alcohol consumption behaviour among Thai men
Alcohol consumption is a major health concern in Thailand contributing to addiction and disease. With 17 million Thai men regularly drinking alcohol, cultural norms and environmental factors influence consumption patterns. Geographic Information Systems (GIS) research has established connections between alcohol outlet density and increased drinking. Using Moran’s I, Local Indicators of Spatial Association (LISA), and spatial regression models, spatial clusters of alcohol consumption were identified across Thai provinces, with Chonburi Province showing the highest rate at 72.2% and Yala the lowest at 28.6%. Regular alcohol consumption among Thai men exhibited a positive spatial correlation, with Moran’s I equal to 0.477. Bivariate analysis found significant spatial autocorrelation between alcohol outlet density (0.301), population density (0.237) and access to medical facilities (0.290), showing high-high clusters in urbanized areas and low-low clusters in southern regions. Spatial regression using the Spatial Lag Model (SLM) demonstrated that alcohol outlet density, population density and the proportion of the population to medical facilities are significant factors influencing alcohol consumption, explaining 49.2% of the variation in alcohol consumption. The findings suggest the need for targeted public health interventions in high-risk areas, especially in regions with dense alcohol outlets and urban populations, alongside developing policies to promote healthier behaviours and limit alcohol access.
A Bayesian spatiotemporal Poisson conditional autoregressive model for dengue haemorrhagic fever in Indonesia integrating satellite-generated environmental data
In association with cases of Dengue Haemorrhagic Fever (DHF), Indonesia’s Breteau Index has consistently fallen below the national standard of 95% over the past 12 years (2007–2019). Currently, the country relies on survey methods to map DHF spread, but these methods are costly and require substantial resource support since monitoring DHF cases necessitates considering both spatial and temporal aspects. As an alternative, we proposed a pilot study utilizing a localized version of the hierarchical Bayesian spatiotemporal conditional autoregressive model (LHBSTCARM) to predict the DHF cases in Makassar City, Indonesia. Using this approach, we examined the relationship between DHF and the normalized difference built-up index (NDBI), the Normalized Difference Vegetation Index (NDVI), and the Normalized Difference Water Index (NDWI) that were downloaded from the Sentinel-2 satellite. Based on these datasets, we identified an optimal LHBSTCARM model that classified areas in Makassar City into distinct spatial risk groups based on the likelihood of dengue occurrence. Specifically, the model identified four districts with low relative risk, one with high relative risk and the remaining districts with moderate relative risk. Incorporating covariates, the model also revealed that NDVI and NDWI were significant predictors for dengue outbreaks, whereas NDBI was not. Both significant covariates showed negative effects, with a one-unit increase in NDVI and NDWI associated with reductions in DHF cases by 84.5% and 81.5%, respectively. Thus, NDVI and NDWI are the environmental variables of choice for the prediction of DHF incidence.
Factors associated with the spatial distribution of leprosy: a systematic review of the published literature
This systematic review aimed to identify factors related to the spatial distribution of leprosy through studies utilising geographic information systems (GIS) techniques. PRISMA 2020 guidelines were adopted and the Population, Concept, Context (PCC) strategy employed to formulate the research question and define its scope: what factors associated with the spatial context of leprosy have been identified in studies utilising GIS techniques, and what are the key contributions of GIS in understanding the disease? The bibliographic databases consulted included PubMed, LILACS, EMBASE and Scopus. Only full original research articles in English, Spanish or Portuguese were included. Of the identified articles, 35 (23.8%) met the inclusion criteria, with the majority addressing socioeconomic factors (60.0%), followed by health indicators (17.1%). A smaller proportion of studies focused on logistics/distance (8.6%) or environmental aspects (2.9%). Although numerous studies utilise GIS techniques for understanding leprosy, few adopt robust methodologies to investigate the factors influencing its spatial features. There is a scarcity of studies employing GIS to examine environmental and logistical aspects related to the spatial distribution of leprosy. Addressing these gaps requires broader dissemination of the potential advantages of GIS in leprosy; the provision of reliable public data; and the capacity building of professionals committed to combating and controlling leprosy in endemic areas.
Evaluation by accessibility index differences of the cross-border potential for general inpatient care in the Ems-Dollart Region, a Dutch-German cross-border area
Access to healthcare in border regions is hampered by the very existence of the border and the limitations of cross-border cooperation between healthcare systems. This work examined the status quo of access to inpatient care at a high level of spatial detail and the potential impact of a cross-border cooperation in the Ems- Dollart border Region (EDR), a region located in the northern Dutch-German border area. A cross-border data model of inpatient care for Germany and The Netherlands was created using hospital beds as supply and 1-km² gridded population data as demand. The enhanced the two-step floating catchment area (E2SFCA) algorithm was applied to match supply and demand using road accessibility as intermediary. The model was calculated both for national and cross-border accessibility scenarios, with results standardised against national averages to account for systemic differences between German and Dutch healthcare settings. The resulting maps of spatial access to inpatient care capacity showed that the region has access rates below the national averages, with access rates in The Netherlands showing greater spatial variation than seen in Germany. The border appeared to be less important as cause of low access rates than other factors, such as the presence of the North Sea coast. The model results for cross-border hospital care showed a very local potential with access gains for only 2.2% of the population in the EDR, mostly in The Netherlands. This increase was drawn from wide areas with average and high access rates from both Germany and The Netherlands.
Socioeconomic determinants of pandemics: a spatial methodological approach with evidence from COVID-19 in Nice, France
During the period 4 January 4 – 14 February 2021 the spread of the COVID-19 epidemic peaked in the city of Nice, France with a worrying number of infected cases. This article focuses on analyzing the explicit, spatial pattern of virus spread and assessing the geographical factors influencing this distribution. Spatial modelling was carried out to examine geographical disparities in terms of distribution, incidence and prevalence of the virus, while taking socio-economic factors into account. A multiple linear regression model was used to identify the key socio-economic variables. Global and local spatial autocorrelation were measured using Moran and LISA indices, followed by spatial autocorrelation analysis of the residuals. Similarly, we used the Geographically Weighted Regression (GWR) model and the Multiscale Geographically Weighted Regression (MGWR) model to assess the influence of socio-economic factors that vary on a global and local scale. Our results reveal a marked geographical polarization, with affluent areas in the Southeast of the city contrasting sharply with disadvantaged neighbourhoods in the Northwest. Neighbourhoods with low Localized Human Development Index (LHDI), low levels of education, social housing and immigrant populations all pointed to worrying values. On the other hand, people who use public transport were significantly more likely to be contaminated by the virus. These results underline the importance of geographically predicting COVID-19 distribution patterns to guide targeted interventions and health policies. Understanding these spatial patterns using models such as MGWR can help guide public health interventions and inform future health policies, particularly in the context of pandemics.
Spatial Bayesian semi-parametric Cox-Leroux modelling of stroke patient hospitalization: aspects on survival
Survival analysis consists of a set of statistical methods used to analyse data where the outcome variable is the time until an event occurs. When such data are collected across distinct spatial regions, incorporating spatial information into survival models can be beneficial. A common approach is to apply an intrinsic Conditional Autoregressive (CAR) prior to an area-level frailty term to account for spatial correlation between regions. We extend the Bayesian Cox semi-parametric model by incorporating a spatial frailty term using the Leroux CAR prior. The aim was to improve the model’s ability to describe stroke hospitalisations at the Stroke Centre Hospital in Makassar, Indonesia with a focus on understanding the geographic distribution of hospitalisations, Length of Stay (LOS) and factors influencing patient outcomes. The dataset was obtained from medical records of stroke patients admitted to this hospital (April 2021-June 2024). Variables included LOS, discharge outcomes, sex, age, stroke type, uric acid levels, hypertension, hypercholesterolemia, and diabetes mellitus. Our findings indicate that diabetes, stroke type and the presence of hypercholesterolemia significantly influence recovery rates in stroke patients. Specifically, patients with diabetes had lower recovery, while those with hypercholesterolemia and ischemic stroke patients had faster recovery compared to those with haemorrhagic strokes.
The effects of population mobility on Chinese HIV epidemics in spill-over and influx risks perspectives: a spatial epidemiology analysis
Investigating the spatial effects of population mobility on Human Immunodeficiency Virus (HIV) epidemics provides valuable insights for effective disease control. Data on the incidence and prevalence of HIV and socioeconomic factors from 2013 to 2022 across 31 provinces in China were collected. The Baidu migration index was employed to construct inter-provincial population migration matrices for spatial lag models to evaluate spatial spill-overs and influx risks associated with HIV epidemics macroscopically. This study also analysed the impacts of socioeconomic variables, conducted robustness tests for validation, and performed subgroup analysis stratified by HIV incidence levels. Significant spatial autocorrelation of HIV morbidity was confirmed by finding a positive Moran’s I. The spatial lag model indicated that when a given province had a 1-unit increase in HIV incidence, its average outflow would cause a 0.7068-unit incidence rate increment in other destination provinces, while every unit increase of HIV incidence in other provinces would induce a 0.7013-unit HIV average incidence rise in the original one when it played the role of destination on average. Furthermore, higher population density and lower educational attainment were associated with elevated HIV incidence (p<0.001). The robustness of the findings was verified, and subgroup analysis indicated that reasons besides population mobility should be given priority consideration in regions with higher HIV incidence. The risks of population mobility related to the HIV epidemic were quantified, highlighting the necessity of developing effective and acceptable HIV prevention and control strategies specifically tailored for migrant populations.
Advanced analysis of depression tendency in China: an investigation of environmental and social factors based on geographical and temporal weighted regression
The spatiotemporal distribution of depressive tendencies across China from 2011 to 2022 was investigated using the Baidu Depression Search Index (BDSI). We examined key influencing natural factors, such as water pollution, air pollution, and deforestation, along with economic indicators, such as gross domestic product per capita, disposable income per capita, and health professionals per 10,000 population. Geographical and Temporal Weighted Regression (GTWR) was applied to capture the spatiotemporal heterogeneity of the BDSI determinants. The results revealed significant regional disparities, with the China’s eastern region consistently exhibiting the highest values reflecting heightened mental health concerns, while the western region were found to have the lowest. The BDSI trends followed different trajectories, all of which peaked in 2019 before a sharp decline in 2020. Water pollution transitioned from negative to positive influence in the East, while deforestation exhibited regionally variable effects. Air pollution, peaking in 2019 and 2022, demonstrated the highest impact variability. The economic indicators showed complex regional and temporal patterns underscoring the need for tailored interventions. Together, these findings provided critical insights into the intricate interplay between environmental, economic, and healthcare factors in shaping mental health that highlighted the necessity of region-specific policies to mitigate depressive tendencies and enhance public mental well-being. These research results offer targeted recommendations for regionally adaptive mental health strategies across China.
Associations between rocky mountain spotted fever and veterinary care access, climatic factors and landscape in the State of Arizona, USA
Rocky Mountain Spotted Fever (RMSF) is a potentially fatal tick-borne disease historically prevalent in the eastern and southeastern U.S. Since the early 2000s, there has been a notable rise in RMSF cases in the south-western U.S. Despite the documented role of dogs in tick-borne disease transmission, research on the influence of other factors, such as veterinary care access, climatic conditions and landscape characteristics on RMSF incidence is limited. This study investigated the combined impact of these factors on RMSF using county-level temperature, relative humidity, precipitation, land cover, dog populations and veterinary care access in Arizona from 2006 to 2021. Employing a spatial negative binomial regression model, the study revealed significant associations between veterinary care access, precipitation, relative humidity, shrubland, and RMSF incidence across three models incorporating lagged effects (0-month, 1-month, and 2-month) for climatic variables. A key finding was that counties experiencing higher veterinary care access were more likely to report lower RMSF case counts (incidence rate ratio (IRR): 0.9237). The mean precipitation consistently showed the highest positive IRR (1.8137) across all models, indicating its strong influence. In contrast, relative humidity (IRR: 0.9413) and shrubland presence (IRR: 0.9265) demonstrated significant negative associations with RMSF incidence. These findings underscore the importance of veterinary care access, climatic factors, and land cover in shaping RMSF dynamics, particularly in regions with increasing incidence rates.
Lung cancer associated with natural vegetation cover: spatial analysis in the state of Pará, eastern Brazil
Lung cancer represents the second-highest incidence of cancer worldwide and the leading cause of cancer-related deaths. Smoking is still the main risk factor, but other factors are also important, such as those associated with the large-scale exploitation of natural resources. This ecological study aimed to analyse the potential association between the spatial distribution of lung cancer and the natural vegetation cover in the state of Pará, Brazil. The study included 700 new cases of lung cancer taken from the Integrador Hospital Cancer Registries, a web-based system consolidating cancer data across Brazil. Spatial exploratory techniques were estimated by global and local spatial correlation coefficients and presented as thematic maps. The independent variables were socio-economic and environmental indicators. A significant variation was identified between different geographical areas and the distribution pattern of lung cancer incidence, with a negative correlation (I = - 0.12, p-value = < 0.001) between cancer rates and natural vegetation cover. The findings provide insights into the role of environmental factors that influence public health, ratifying the need for environmental conservation policies to promote health and prevent disease.
Use of geocoding techniques for epidemiological surveillance in the Federal District, Brazil: a case study using dengue
This study aimed to compare different address geocoding services and their applicability to epidemiological surveillance using dengue as an example. We applied a cross-sectional, descriptive study based on case notifications in the Notifiable Diseases Information System (SINAN) for the Brazilian capital in 2014 that includes complete postal code (CEP) information identified in the National Address Database for Statistical Purposes (CNEFE), which is considered the ‘gold standard’ for accuracy analysis. For records without CEP, georeferencing was performed through linkage of the original database with four geocoding tools: Google Maps, CNEFE, OpenStreetMap (OSM) and ArcGIS. Variables used for georeferencing were ‘street name’, ‘code for municipality/ city of residency’ and ‘State’ using accuracy rate estimate and mean spatial error (MSE) of case locations. The two most accurate models were used for kernel density (KD) analysis which is valuable for identifying priority areas for intervention. There were 18,206 dengue cases, 109 (0.6%) of which had correct CEP information and geocoded using CNEFE bases. The linkage results showed that Google Maps application programming interface (API) had an accuracy of 17.6% (MSE: 178.89km), CNEFE 9.0% (MSE: 17.24km), OSM 7.1% (MSE: 564.19km), and ArcGIS 3.7% (MSE: 2001.33km). Although overall accuracy values were modest, the best two models proven to be effective for KD analysis revealed similar patterns between Google Maps and CNEFE results but choosing the preferable geocoding technique should also financial resources. This study recommends the use of Google Maps API for georeferencing, followed by CNEFE.
Spatial analysis of congenital heart disease in São Paulo State, Brazil 2012-2022: associations with air pollution, maternal factors and social vulnerability
Congenital Heart Disease (CHD) is a major cause of neonatal and infant morbidity and mortality and it has a multifactorial aetiology. This study aimed to analyse the spatial association between exposure to air pollutants during the first trimester of pregnancy, social vulnerability, and maternal factors with the occurrence of CHD between 2012 and 2022 in the state of São Paulo, Brazil. Data were obtained from the live birth information system for maternal outcomes and characteristics, the São Paulo social vulnerability index as a contextual indicator, and concentrations of fine particulate matter (PM2.5), Carbon Monoxide (CO) and ozone, estimated using the Copernicus Atmosphere Monitoring Service (CAMS-EAC4) reanalysis dataset of environmental exposure. A Bayesian hierarchical spatial model with a Besag–York– Mollié 2 (BYM2) specification was applied using the INLA approach. The results showed that exposure to PM2.5 was significantly associated with an increased risk of CHD (RR = 1.022; 95% CrI: 1.005-1.040), as were advanced maternal age (>35 years) (RR = 1.649; 95% CrI: 1.587-1.715) and inadequate prenatal care (RR = 1.112; 95% CrI: 1.070-1.155). Conversely, municipalities classified as having medium (RR = 0.757; 95% CrI: 0.641-0.894) and high social vulnerability (RR = 0.643; 95% CrI: 0.492-0.844) showed a significantly lower adjusted risk compared to those with low vulnerability. No significant associations were identified for CO or ozone. Spatial analysis revealed persistently high risks in municipalities within the São Paulo Metropolitan Region, even after adjusting for environmental and socio-demographic variables, highlighting population profiles and priority areas for public health surveillance and targeted interventions.
A post-pandemic analysis of air pollution over small-sized urban areas in southern Thailand following the COVID-19 lockdown
COVID-19 has been a pandemic with paramount effects on human health that brought about a noticeable improvement of air quality due to a reduction of anthropogenic activities. While studying this phenomenon in large cities has been a popular research topic, related research on smaller-sized urban areas has not been given the necessary attention. In the current study, we focus on the period during and after the COVID-19 pandemic over 8 small- and medium-sized urban areas in southern Thailand and present the effect of the lockdown on the air quality as quantified by the Sentinel-5P satellite and regulatory-grade surface stations over the years 2020, 2021 and 2022. Findings indicate that there is a noticeable reduction of -14%, -24% and -28% for NO2, PM2.5 and PM10 surface concentrations, respectively, for all the 8 urban areas cumulatively for the 2-month period following the lockdown, while results for O3 were inconclusive. An alignment between the ground and satellite observations is noticed, despite their difference in spatial scales and measuring different physical characteristics. Regression analysis between the single-pixel values over the ground station locations and the spatially-averaged pixels over the urban extent indicates an agreement between these two features, suggesting that single measurements can be representative of the air pollution status for relatively small-sized urban areas.