Tracing vulnerable communities to ambient air pollution exposure: A geodemographic and remote sensing approach.

Most studies analyzing the effects of air pollution on disadvantaged populations use ground air quality measurements. However, ground stations are generally limited, with nearly 40% of countries having no official PM2.5 stations, not allowing air quality analysis for a significantly large share of the world's population. Furthermore, limited studies analyze community data from a geodemographic perspective, in other words, to delineate the sociodemographic profiles and geographically locate the socioeconomic groups more exposed to ambient air pollution. Therefore, a significant question arises: How can we trace vulnerable communities to air pollution in areas lacking air-quality ground data? Here, we propose a novel methodology to respond to this question. We use NO2, SO2, CO, and HCHO tropospheric column air-quality data from Sentinel-5P, a satellite that quantifies concentrations of atmospheric species from space operationally. We integrate them with census and environmental data and apply the local fuzzy geographically weighted clustering spatial machine learning method for segmentation analysis. Our findings for Bali, Indonesia, provide quantitative evidence for the benefits of this methodology in tracing and delineating the profiles of the communities most exposed to air pollution. For example, results show that communities with highly disadvantaged populations, such as unemployed (over 27.8%), low educated (over 27.9%), and children (over 22.1%) (mainly located around Bali's south and north coast touristic areas), exhibit very high values (over the 75th quartile) across the pollutants studied. The proposed method is reproducible easily, quickly, and at low cost, as it is based on freely available satellite data and not on costly ground station measurements. This will hopefully assist decision-makers in tracing the most vulnerable subpopulations, even in areas with inadequate air-quality monitoring networks, thus allowing local governments around the globe (even those that are financially weak) to achieve environmental justice and their sustainable development goals.

Exploring the socioeconomic drivers of COVID-19 mortality across various spatial regimes.

Identifying the socioeconomic drivers of COVID-19 deaths is essential for designing effective policies and health interventions. However, how the significance and impact of these factors varies across different spatial regimes has been scantly explored. In this ecological cross-sectional study, we apply the spatial lag by regimes regression model to examine how the socioeconomic and health determinants of COVID-19 death rate vary across (a) metropolitan vs. non-metropolitan, (b) shelter-in-place vs. no-shelter-in-place order, and (c) Democratic vs. Republican US counties. A total of 20 variables were studied across 3108 counties in the contiguous US for the first year of the pandemic (6 February 2020 to 5 February 2021). The results show that the COVID-19 death rate not only depends on a complex interplay of the population demographic, socioeconomic and health-related characteristics, but also on the spatial regime that the residents live, work and play. Household median income, household size, percentage of African Americans, percentage aged 40-59 and heart disease mortality are significant to metropolitan but not to non-metropolitan counties. We identified lack of insurance access as a significant driver across all regimes except for Democratic. We also showed that the political orientation of the governor might have impacted COVID-19 death rates due to the public response (i.e., shelter-in-place vs. no-shelter-in-place order). The proposed analysis allows for understanding the socioeconomic context in which public health policies can be applied, and importantly, it presents how COVID-19 death related factors vary across different spatial regimes.

Greenness-air pollution-physical activity-hypertension association among middle-aged and older adults: Evidence from urban and rural China.

BACKGROUND: Although some evidence suggests that residential greenness may prevent hypertension in urban areas, limited attention has been paid to urban-rural disparities in the association of greenness with hypertension in rapidly urbanizing developing countries. OBJECTIVES: The current study investigated the association between the amount of neighbourhood greenness and hypertension among middle and older aged people in Chinese urban and rural areas. It further examined whether PM2.5 (particulate matter ≤2.5 µm in aerodynamic diameter) concentrations, physical activity, and body mass index (BMI) mediated the association of greenness with hypertension. METHODS: We used data from 11 486 adults aged 50 years or above within the first wave of the Study on Global Ageing and Adult Health in China during 2007-2010. Hypertension was assessed by criterion-based measures of blood pressure. Residential greenness was characterized by satellite-derived Normalized Difference Vegetation Index. We employed multilevel generalized structural equation models to estimate the association between neighbourhood greenness and hypertension in urban and rural areas. Serial mediation models have been performed to test potential pathways linking greenness to hypertension. RESULTS: In rural areas, a greater amount of residential greenness was directly associated with a decrease in the odds of hypertension (odds ratio = 0.51, 95% confidence interval 0.29-0.89). No direct association was observed in urban areas (odds ratio = 1.33, 95% confidence interval 0.94-1.89). Serial mediation models showed that the association of greenness with hypertension was completely mediated by PM2.5 concentrations in urban areas, while the association of greenness with hypertension was only partially mediated by PM2.5 concentrations and serial PM2.5 concentrations-physical activity path in rural areas. There was no evidence that physical activity, air pollution-BMI path, air pollution-physical activity-BMI path, and physical activity-BMI path mediated the association in both urban and rural areas. CONCLUSIONS: Higher neighbourhood greenness was directly associated with a lower prevalence of hypertension among middle and older aged adults in rural China but not in urban areas. The association of greenness with hypertension was completely mediated by air pollution (without any mediation effect of physical activity and BMI) in urban areas. In contrast, the association was partly mediated by air pollution, physical activity, and other unobservables in rural areas. Further longitudinal studies are warranted to prove a cause-and-effect association, which may help policymakers and practitioners to conduce effective interventions to prevent and control the prevalence of hypertension and the attendant disease burden.

Information Extraction and Population Estimates of Settlements from Historic Corona Satellite Imagery in the 1960s.

The Corona satellite program was a historic reconnaissance mission which provided high spatial resolution panchromatic images during the Cold War era. Nevertheless, and despite the historic uniqueness and importance of the dataset, efforts to extract tangible information from this dataset have primarily focused on visual interpretation. More sophisticated approaches have been either hampered or unrealized, often justified by the primitive quality of this early satellite product. In the current study we attempt to showcase the usability of Corona imagery outside the context of visual interpretation. Using a 1968 Corona image acquired over the city municipality of Plovdiv, Bulgaria, we reconstruct a panchromatic 1.8 m spatial resolution georegistered image with a relative displacement Root Mean Square Error (RMSE) of 6.616 (for x dimension) and 1.886 (for y dimension) and employ segmentation and texture analysis to discern agricultural parcels and settlements' footprints. Population statistics of this past era are retrieved from national census and related to settlements' footprints. An exponential relationship between the two variables was identified by applying a semi-log regression. The high adjusted R2 value found (76.54%) indicates that Corona images offer a unique opportunity for population data analysis of the past. Overall, we showcase that the Corona images' usability extends beyond the visual interpretation, and features of interest extracted through image analysis can be subsequently used for further geographical and historical research.

Mapping the geodemographics of racial, economic, health, and COVID-19 deaths inequalities in the conterminous US.

A large number of studies have examined individual-level factors that increase COVID-19 fatalities. However, no research has focused on the geodemographic classification of the most susceptible communities to COVID-19. In this cross-sectional ecological study, we used local fuzzy geographically-weighted clustering to create the socioeconomic profile of the US counties in relation to COVID-19 death rates. We demonstrate that living in a county which has households with lower income, people with a lack of health insurance, a high African-American percentage, and lower education level, lead to 27.12% higher COVID-19 death rates than the national median, and 72.56% higher compared to the least vulnerable counties. Compared to counties with a high COVID-19 death rate, counties with a low COVID-19 death rate have 44.90% higher annual median household income and nearly double house worth (89.51% more). Results show that the effects of the COVID-19 pandemic are not universal and that the minoritised and impoverished populations suffer more. Our analysis can effectively pinpoint the most vulnerable counties and importantly allows for understanding the socioeconomic context in which tailored interventions can be applied to mitigate COVID-19 deaths.

Ranking the importance of demographic, socioeconomic, and underlying health factors on US COVID-19 deaths: A geographical random forest approach.

A growing number of studies show that the uneven spatial distribution of COVID-19 deaths is related to demographic and socioeconomic disparities across space. However, most studies fail to assess the relative importance of each factor to COVID-19 death rate and, more importantly, how this importance varies spatially. Here, we assess the variables that are more important locally using Geographical Random Forest (GRF), a local non-linear regression method. Through GRF, we estimated the non-linear relationships between the COVID-19 death rate and 29 socioeconomic and health-related factors during the first year of the pandemic in the USA (county level). GRF outputs are compared to global (Random Forest and OLS) and local (Geographically Weighted Regression) models. Results show that GRF outperforms all models and that the importance of variables highly varies by location. For example, lack of health insurance is the most important factor in one-third (34.86%) of the US counties. Most of these counties are (concentrated mainly in the Midwest region and South region). On the other hand, no leisure-time physical activity is the most important primary factor for 19.86% of the US counties. These counties are found in California, Oregon, Washington, and parts of the South region. Understanding the location-based characteristics and spatial patterns of socioeconomic and health factors linked to COVID-19 deaths is paramount for policy designing and decision making. In this way, interventions can be designed and implemented based on the most important factors locally, avoiding thus general guidelines addressed for the entire nation.

Digital contact tracing, community uptake, and proximity awareness technology to fight COVID-19: a systematic review.

Digital contact tracing provides an expeditious and comprehensive way to collect and analyze data on people's proximity, location, movement, and health status. However, this technique raises concerns about data privacy and its overall effectiveness. This paper contributes to this debate as it provides a systematic review of digital contact tracing studies between January 1, 2020, and March 31, 2021. Following the PRISMA protocol for systematic reviews and the CHEERS statement for quality assessment, 580 papers were initially screened, and 19 papers were included in a qualitative synthesis. We add to the current literature in three ways. First, we evaluate whether digital contact tracing can mitigate COVID-19 by either reducing the effective reproductive number or the infected cases. Second, we study whether digital is more effective than manual contact tracing. Third, we analyze how proximity/location awareness technologies affect data privacy and population participation. We also discuss proximity/location accuracy problems arising when these technologies are applied in different built environments (i.e., home, transport, mall, park). This review provides a strong rationale for using digital contact tracing under specific requirements. Outcomes may inform current digital contact tracing implementation efforts worldwide regarding the potential benefits, technical limitations, and trade-offs between effectiveness and privacy.

Sustainable development under population pressure: lessons from developed land consumption in the conterminous U.S.

Population growth will result in a significant anthropogenic environmental change worldwide through increases in developed land (DL) consumption. DL consumption is an important environmental and socioeconomic process affecting humans and ecosystems. Attention has been given to DL modeling inside highly populated cities. However, modeling DL consumption should expand to non-metropolitan areas where arguably the environmental consequences are more significant. Here, we study all counties within the conterminous U.S. and based on satellite-derived product (National Land Cover Dataset 2001) we calculate the associated DL for each county. By using county population data from the 2000 census we present a comparative study on DL consumption and we propose a model linking population with expected DL consumption. Results indicate distinct geographic patterns of comparatively low and high consuming counties moving from east to west. We also demonstrate that the relationship of DL consumption with population is mostly linear, altering the notion that expected population growth will have lower DL consumption if added in counties with larger population. Added DL consumption is independent of a county's starting population and only dependent on whether the county belongs to a Metropolitan Statistical Area (MSA). In the overlapping MSA and non-MSA population range there is also a constant DL efficiency gain of approximately 20 km2 for a given population for MSA counties which suggests that transitioning from rural to urban counties has significantly higher benefits in lower populations. In addition, we analyze the socioeconomic composition of counties with extremely high or low DL consumption. High DL consumption counties have statistically lower Black/African American population, higher poverty rate and lower income per capita than average in both NMSA and MSA counties. Our analysis offers a baseline to investigate further land consumption strategies in anticipation of growing population pressures.