Spatial association between COVID-19 incidence rate and nighttime light index.

This study statistically identified the localised association between socioeconomic conditions and the coronavirus disease 2019 (COVID-19) incidence rate in Thailand on the basis of the 1,727,336 confirmed cases reported nationwide during the first major wave of the pandemic (March-May 2020) and the second one (July 2021-September 2021). The nighttime light (NTL) index, formulated using satellite imagery, was used as a provincial proxy of monthly socioeconomic conditions. Local indicators of spatial association statistics were applied to identify the localised bivariate association between COVID-19 incidence rate and the year-on-year change of NTL index. A statistically significant negative association was observed between the COVID-19 incidence rate and the NTL index in some central and southern provinces in both major pandemic waves. Regression analyses were also conducted using the spatial lag model (SLM) and the spatial error model (SEM). The obtained slope coefficient, for both major waves of the pandemic, revealed a statistically significant negative association between the year-on-year change of NTL index and COVID-19 incidence rate (SLM: coefficient= âˆ'0.0078 and âˆ'0.0064 with P<0.001 and 0.056, respectively; and SEM: coefficient= âˆ'0.0086 and âˆ'0.0083 with P=0.067 and 0.056, respectively). All of the obtained results confirmed the negative association between the COVID-19 pandemic and socioeconomic activity revealing the future extensive applications of satellite imagery as an alternative data source for the timely monitoring of the multidimensional impacts of the pandemic.

Patterns of exposure to SARS-CoV-2 carriers manifest multiscale association between urban landscape morphology and human activity.

The outbreak of the Coronavirus disease 2019 (COVID-19), and the drastic measures taken to mitigate its spread through imposed social distancing, have brought forward the need to better understand the underlying factors controlling spatial distribution of human activities promoting disease transmission. Focusing on results from 17,250 epidemiological investigations performed during early stages of the pandemic outbreak in Israel, we show that the distribution of carriers of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), which causes COVID-19, is spatially correlated with two satellite-derived surface metrics: night light intensity and landscape patchiness, the latter being a measure to the urban landscape's scale-dependent spatial heterogeneity. We find that exposure to SARS-CoV-2 carriers was significantly more likely to occur in "patchy" parts of the city, where the urban landscape is characterized by high levels of spatial heterogeneity at relatively small, tens of meters scales. We suggest that this spatial association reflects a scale-dependent constraint imposed by the city's morphology on the cumulative behavior of the people inhabiting it. The presented results shed light on the complex interrelationships between humans and the urban landscape in which they live and interact, and open new avenues for implementation of multi-satellite data in large scale modeling of phenomena centered in urban environments.

Global fine-scale changes in ambient NO2 during COVID-19 lockdowns.

Nitrogen dioxide (NO2) is an important contributor to air pollution and can adversely affect human health1-9. A decrease in NO2 concentrations has been reported as a result of lockdown measures to reduce the spread of COVID-1910-20. Questions remain, however, regarding the relationship of satellite-derived atmospheric column NO2 data with health-relevant ambient ground-level concentrations, and the representativeness of limited ground-based monitoring data for global assessment. Here we derive spatially resolved, global ground-level NO2 concentrations from NO2 column densities observed by the TROPOMI satellite instrument at sufficiently fine resolution (approximately one kilometre) to allow assessment of individual cities during COVID-19 lockdowns in 2020 compared to 2019. We apply these estimates to quantify NO2 changes in more than 200 cities, including 65 cities without available ground monitoring, largely in lower-income regions. Mean country-level population-weighted NO2 concentrations are 29% ± 3% lower in countries with strict lockdown conditions than in those without. Relative to long-term trends, NO2 decreases during COVID-19 lockdowns exceed recent Ozone Monitoring Instrument (OMI)-derived year-to-year decreases from emission controls, comparable to 15 ± 4 years of reductions globally. Our case studies indicate that the sensitivity of NO2 to lockdowns varies by country and emissions sector, demonstrating the critical need for spatially resolved observational information provided by these satellite-derived surface concentration estimates.

Changes in turbidity and human activities along Haihe River Basin during lockdown of COVID-19 using satellite data.

During the outbreak of the COVID-19, China implemented an urban lockdown in the first period. These measures not only effectively curbed the spread of the virus but also brought a positive impact on the ecological environment. The water quality of urban inland river has a significant impact on urban ecology and public health. This study uses Sentinel-2 visible and near-infrared band reflectance and the Normalized Difference Turbidity Index (NDTI) to analyze the water quality of the Haihe River Basin during the control period of COVID-19. It is found that during the lockdown period, the river water quality was significantly improved compared to the same period in 2019. The average NDTI of the Haihe River Basin in March decreased by 0.27, a decrease of 219.06%; in April, it increased by 0.07, that is 38.38%. Further exploration using VIIRS lights found that the brightness of the lights in the main urban area was significantly lower in February, the beginning of the lockdown. However, as the city was unblocked, the lights rose sharply in March and then recovered to normal. There is obvious asynchrony in changes between river turbidity and light. The results can help understand the impact of human activities on the natural environment.

Monitoring Cyanobacterial Blooms during the COVID-19 Pandemic in Campania, Italy: The Case of Lake Avernus.

Cyanobacteria are ubiquitous photosynthetic microorganisms considered as important contributors to the formation of Earth's atmosphere and to the process of nitrogen fixation. However, they are also frequently associated with toxic blooms, named cyanobacterial harmful algal blooms (cyanoHABs). This paper reports on an unusual out-of-season cyanoHAB and its dynamics during the COVID-19 pandemic, in Lake Avernus, South Italy. Fast detection strategy (FDS) was used to assess this phenomenon, through the integration of satellite imagery and biomolecular investigation of the environmental samples. Data obtained unveiled a widespread Microcystis sp. bloom in February 2020 (i.e., winter season in Italy), which completely disappeared at the end of the following COVID-19 lockdown, when almost all urban activities were suspended. Due to potential harmfulness of cyanoHABs, crude extracts from the "winter bloom" were evaluated for their cytotoxicity in two different human cell lines, namely normal dermal fibroblasts (NHDF) and breast adenocarcinoma cells (MCF-7). The chloroform extract was shown to exert the highest cytotoxic activity, which has been correlated to the presence of cyanotoxins, i.e., microcystins, micropeptins, anabaenopeptins, and aeruginopeptins, detected by molecular networking analysis of liquid chromatography tandem mass spectrometry (LC-MS/MS) data.

Excess mortality during the COVID-19 pandemic: a geospatial and statistical analysis in Aden governorate, Yemen.

BACKGROUND: The burden of COVID-19 in low-income and conflict-affected countries remains unclear, largely reflecting low testing rates. In parts of Yemen, reports indicated a peak in hospital admissions and burials during May-June 2020. To estimate excess mortality during the epidemic period, we quantified activity across all identifiable cemeteries within Aden governorate (population approximately 1 million) by analysing very high-resolution satellite imagery and compared estimates to Civil Registry office records. METHODS: After identifying active cemeteries through remote and ground information, we applied geospatial analysis techniques to manually identify new grave plots and measure changes in burial surface area over a period from July 2016 to September 2020. After imputing missing grave counts using surface area data, we used alternative approaches, including simple interpolation and a generalised additive mixed growth model, to predict both actual and counterfactual (no epidemic) burial rates by cemetery and across the governorate during the most likely period of COVID-19 excess mortality (from 1 April 2020) and thereby compute excess burials. We also analysed death notifications to the Civil Registry office over the same period. RESULTS: We collected 78 observations from 11 cemeteries. In all but one, a peak in daily burial rates was evident from April to July 2020. Interpolation and mixed model methods estimated ≈1500 excess burials up to 6 July, and 2120 up to 19 September, corresponding to a peak weekly increase of 230% from the counterfactual. Satellite imagery estimates were generally lower than Civil Registry data, which indicated a peak 1823 deaths in May alone. However, both sources suggested the epidemic had waned by September 2020. DISCUSSION: To our knowledge, this is the first instance of satellite imagery being used for population mortality estimation. Findings suggest a substantial, under-ascertained impact of COVID-19 in this urban Yemeni governorate and are broadly in line with previous mathematical modelling predictions, though our method cannot distinguish direct from indirect virus deaths. Satellite imagery burial analysis appears a promising novel approach for monitoring epidemics and other crisis impacts, particularly where ground data are difficult to collect.

The implication of the air quality pattern in South Korea after the COVID-19 outbreak.

By using multiple satellite measurements, the changes of the aerosol optical depth (AOD) and nitrogen dioxide (NO2) over South Korea were investigated from January to March 2020 to evaluate the COVID-19 effect on the regional air quality. The NO2 decrease in South Korea was found but not significant, which indicates the effects of spontaneous social distancing under the maintenance of ordinary life. The AODs in 2020 were normally high in January, but they became lower starting from February. Since the atmosphere over Eastern Asia was unusually stagnant in January and February 2020, the AOD decrease in February 2020 clearly reveals the positive effect of the COVID-19. Considering the insignificant NO2 decrease in South Korea and the relatively long lifetime of aerosols, the AOD decrease in South Korea may be more attributed to the improvement of the air quality in neighboring countries. In March, regional atmosphere became well mixed and ventilated over South Korea, contributing to large enhancement of air quality. While the social activity was reduced after the COVID-19 outbreak, the regional meteorology should be also examined significantly to avoid the biased evaluation of the social impact on the change of the regional air quality.

Using 164 Million Google Street View Images to Derive Built Environment Predictors of COVID-19 Cases.

The spread of COVID-19 is not evenly distributed. Neighborhood environments may structure risks and resources that produce COVID-19 disparities. Neighborhood built environments that allow greater flow of people into an area or impede social distancing practices may increase residents' risk for contracting the virus. We leveraged Google Street View (GSV) images and computer vision to detect built environment features (presence of a crosswalk, non-single family home, single-lane roads, dilapidated building and visible wires). We utilized Poisson regression models to determine associations of built environment characteristics with COVID-19 cases. Indicators of mixed land use (non-single family home), walkability (sidewalks), and physical disorder (dilapidated buildings and visible wires) were connected with higher COVID-19 cases. Indicators of lower urban development (single lane roads and green streets) were connected with fewer COVID-19 cases. Percent black and percent with less than a high school education were associated with more COVID-19 cases. Our findings suggest that built environment characteristics can help characterize community-level COVID-19 risk. Sociodemographic disparities also highlight differential COVID-19 risk across groups of people. Computer vision and big data image sources make national studies of built environment effects on COVID-19 risk possible, to inform local area decision-making.

COVID-19 lockdown measures reveal human impact on water transparency in the Venice Lagoon.

The lagoon of Venice has always been affected by the regional geomorphological evolution, anthropogenic stressors and global changes. Different morphological settings and variable biogeophysical conditions characterize this continuously evolving system that rapidly responds to the anthropic impacts. When the lockdown measures were enforced in Italy to control the spread of the SARS-CoV-2 infection on March 10th 2020, the ordinary urban water traffic around Venice, one of the major pressures in the lagoon, came to a halt. This provided a unique opportunity to analyse the environmental effects of restrictions to mobility on water transparency. Pseudo true-colour composites Sentinel-2 satellite imagery proved useful for qualitative visual interpretation, showing the reduction of the vessel traffic and their wakes from the periods before and during the SARS-CoV-2 outbreak. A quantitative analysis of suspended matter patterns, based on satellite-derived turbidity, in the absence of traffic perturbations, allowed to focus on natural processes and the residual stress from human activities that continued throughout the lockdown. We conclude that the high water transparency can be considered as a transient condition determined by a combination of natural seasonal factors and the effects of COVID-19 restrictions.