Cyanobacterial Harmful Algal Blooms (CyanoHABs) are progressively becoming a major water quality and public health hazard worldwide. Untreated CyanoHABs can severely affect human health due to their toxin producing ability, causing physiological and neurological disorders such as non-alcoholic liver disease, dementia to name a few. Transfer of these cyanotoxins via food-chain only accelerates public health hazards. CyanoHABs can potentially also lead to a decline in aquatic and animal life, hampering recreational activities at waterbodies and ultimately affecting the country’s economy gravely. CyanoHABs require nutrient rich warm aquatic environments to bloom and their proliferation in increasingly warmer areas of the world can be an indirect indicator of global climate change. Many lakes in the United States have been experiencing such CyanoHABs in the summers, which only grow severe every coming year, and this is consistently leading to increased public health implications. A recent study (September, 2021) by the Centre for Disease Control quantified hospital visits with the trend of such CyanoHABs to indeed observe a strong correlation between the two. This necessitates a need for a user-friendly and accessible infrastructure to monitor inland and coastal waterbodies throughout the U.S for such blooms. We present a remote sensing-based approach wrapped in a lucid web-app, “CyanoTRACKER”, which can help detect CyanoHABs on a global level and act as an early warning system, potentially preventing/lessening public health implications. CyanoHABs are dominated by the Phycocyanin pigment, which absorbs sunlight strongly around 620 nm wavelength. Owing to this specific absorption characteristic and the availability of a satellite band at exactly 620 nm, we use the opensource Sentinel-3 OLCI satellite data to detect the presence of CyanoHABs. CyanoTracker is a user-friendly Google Earth Engine dashboard, which is easily accessible via only a browser and an internet connection and allows for a variety of near-daily analysis options such as: a) select any location throughout the world and view satellite image based on date-range of choice, b) click on any pixel in the satellite image and detect presence/absence of cyanobacteria, c) visualize the spatial spread as well as the temporal phenology of an ongoing bloom or a potential incoming bloom. This dashboard is easily accessible to water-managers and in fact, anyone who wishes to use it with minimal training and can effectively serve as an early warning system to CyanoHAB induced disease outbreaks.
Inspired by Comrie (2021), we discuss a few issues related to dust storms and Coccidioidomycosis (Valley fever). There is inconsistency in the term “dust storm” as used by science communities, and the dust data from NOAA Storm Events Database are from diverse sources, unsuitable for assessing dust-Coccidioidomycosis relationships. Population exposure to dust or Coccidioides needs to consider the frequency, magnitude, duration, and spatial coverage of dust events. Given abundant evidence that dust storms are a viable driver to transport Coccidioides, it is in best public interest to advocate that dust storms may put people at risk for contracting Valley fever.
Being in an arid zone that is frequently submitted to high winds, south-central Arizona regularly gets impacted by several blowing dust events or dust storms every year. Major consequences of these events are visibility impairment and ensuing road traffic accidents, and a variety of health issues induced by inhalation of polluted air loaded with fine particulate matter produced by wind erosion. Despite such problems, and thus a need for guidance on mitigation efforts, studies dealing with dust source attribution for the region are largely missing. Furthermore, existing dust models exhibit large uncertainties and deficiencies in simulating dust events, rendering them of limited use in attribution studies or early warning systems. Therefore, to address some of these model issues, we have developed a high-resolution (1 km) dust modeling system by building upon an existing modeling framework consisting of Weather Research and Forecasting (WRF), FENGSHA (a dust emission model), and Community Multiscale Air Quality (CMAQ) models. In addition to incorporating new representations in the dust emission scheme, including roughness correction factor, sandblasting efficiency, and dust source mask, we implemented, in the dust model, up-to-date and very high-resolution data on land use, soil texture, and vegetation index. We used the revised dust modeling system to simulate a springtime dust storm (08–09 April 2013) of relatively long duration that caused a regional traffic incident involving minor injuries. The model simulations compared reasonably well against observations of concentration of particulate matter with a diameter of 10 μm and smaller (PM₁₀) and satellite-derived dust optical depth and vertical profile of aerosol subtypes. Interestingly, simulation results revealed that the anthropogenic (cropland) dust sources contributed more than half (~53 % or 260 µg/m³) of total PM₁₀, during the dust storm, over the region including Phoenix and western Pinal County. Contrary to the conventional wisdom that desert is the main dust source, our findings for this region challenge such belief and suggest that the regional air quality modeling over dryland regions should emphasize an improved representation of dust from agricultural lands as well, especially during high wind episodes. Such representations have the potential to inform decision-making in order to reduce windblown dust-related hazards on public health and safety.
Abstract It has been debated globally that the COVID-19 lockdown had significantly diminished the emission levels of anthropogenic greenhouse gases (GHGs). However, different countries possess different footprints of GHGs emission. In regions with inconsistent air quality observation, spaceborne sensors can provide synoptic assessment of air quality with time-based environmental decision making. In this study, we utilised satellite data to quantify the temporal dynamics of carbon monoxide (CO) and nitrogen dioxide (NO2) between the pre-lockdown (January–March 2020), lockdown (April–July 2020) and post-lockdown (August–September 2020) periods in Nigeria. Periodic TROPOspheric Monitoring Instrument (TROPOMI) datasets were acquired from the Google Earth Engine Sentinel-5 Explorer and the Copernicus Open Access Hub. The Population-Weighted Mean Concentration (PWEC) of CO and NO2 was computed using raster-based population data and place-based air quality estimates. The associated economic correlates were computed using data mined from TROPOMI and available health records of Nigeria. Satellite data analysis showed that aggregate CO reduced by 35.1% (25.32⋅105 tons) and 9.06% (6.54⋅105 tons) and NO2 plummeted by 32.81% (22,500 tons) and 11.63% (5,360 tons) during the lockdown and post-lockdown periods across the 36 States of the country. While mobility rate dwindled substantially, mortality rate savings from the exposure to damaging effects of the GHGs were roughly $ 14 million (CO) and $10 million (NO2). The fluxes in CO and NO2 suggest that anthropogenic interference in air quality accounting can aid the understanding of the convoluted human–nature relationships for sustainable environmental management.
Field kits for testing the level of a toxicant in the environment are inherently less accurate than a laboratory instrument. Using a specific example, we argue here that kit measurements still have a key role to play when the spatial distribution of a toxicant is very heterogeneous. The context is provided by the groundwater arsenic problem in Bangladesh. We combine here two data sets, a blanket survey of 6595 wells over a 25 km2 based on laboratory measurements and 900 paired kit and laboratory measurements from the same area. We explore different hypothetical mitigation scenarios based on actual data that rely on households with a high-arsenic well switching to a nearby low-arsenic well. We show that the decline in average exposure to arsenic from relying on kit rather than laboratory data is modest in relation to the logistical and financial challenge of delivering exclusively laboratory data. Our analysis indicates that the 50 ug/L threshold used in Bangladesh to distinguish safe and unsafe wells, rather than the WHO guideline of 10 ug/L, is close to optimal in terms of average exposure reduction. We also show, however, that providing kit data at the maximum possible resolution rather than merely classifying wells as unsafe or safe would be even better. These findings are relevant as the government of Bangladesh is about to launch a new blanket testing campaign of millions of wells using field kits.
The COVID-19 pandemic has increased the risk of global public health and has the potential to cause severe food and water insecurity due to economic recession during lockdown for people living in low-middle income countries like Bangladesh where capital resources are scarce. There is growing evidence that household food and water insecurity has been associated with poor psychological outcomes. The objective of this study was to determine the association between household food and water insecurity with mental health and whether these differed among urban-rural households. A cross-sectional online survey was conducted with 545 participants immediately after the COVID-19 lockdown period in Bangladesh (August 1-September 30, 2020). Household food and water security were determined using a 9-item Household Food Insecurity Access Scale (HFIAS) (score range 0-27) and a 12-item Household Water Insecurity Experiences (HWISE) scale (score range 0-36), respectively. The Perceived Stress Scale (PSS) was used to evaluate mental health. Multivariable logistic regression examined the association between household food and water insecurity with perceived stress, adjusting socioeconomic characteristics. An urban-rural stratified analysis was also performed. About 72.84% (397) respondents reported high stress and more than 70% of households suffered from food and water insecurity during the lockdown period. After adjusting covariates, logistic regression model results show that food insecurity was associated with a 1.07-point increase in high perceived stress (OR=1.07, 95% CI=1.01-1.11, p<0.01) while water insecurity was associated with 1.03 times greater odds of high perceived stress (OR=1.03, 95% CI=0.93-1.23, p<0.05). In stratified analysis, only food insecurity was associated with high perceived stress in the urban household (OR=1.08, 95% CI=1.00-1.11, p<0.05). However, none of the household insecurity was associated with perceived stress in rural households. Interventions that promote equal access to resources for low-income individuals will likely to be more effective to alleviate economic burden of pandemic.
Brazil has been severely affected by the COVID-19 pandemic. Temperature and humidity have been purported as drivers of SARS-CoV-2 transmission, but no consensus has been reached in the literature regarding the relative roles of meteorology, governmental policy, and mobility on transmission in Brazil. We compiled data on meteorology, governmental policy, and mobility in Brazil’s 26 states and one federal district from June 2020 to August 2021. Associations between these variables and the time-varying reproductive number (Rt) of SARS-CoV-2 were examined using generalized additive models fit to data from the entire fifteen-month period and several shorter, three-month periods. Accumulated local effects and variable importance metrics were calculated to analyze the relationship between input variables and Rt. We found that transmission is strongly influenced by unmeasured sources of between-state heterogeneity and the near-recent trajectory of the pandemic. Increased temperature generally was associated with decreased transmission and specific humidity with increased transmission. However, the impact of meteorology, policy, and mobility on Rt varied in direction, magnitude, and significance across our study period. This time variance could explain inconsistencies in the published literature to date. While meteorology weakly modulates SARS-CoV-2 transmission, daily or seasonal weather variations alone will not stave off future surges in COVID-19 cases in Brazil. Investigating how the roles of environmental factors and disease control interventions may vary with time should be a deliberate consideration of future research on the drivers of SARS-CoV-2 transmission.
Exposure to fine particulate matter (PM2.5) air pollution is associated with large-scale health consequences, but the uncertainties in estimates of PM2.5-related global premature mortality remain understudied. Using four observation-based PM2.5 datasets and six Coupled Model Intercomparison Project Phase 6 (CMIP6) climate models, we compare uncertainties in current PM2.5-related mortality estimates to the impacts of emissions reductions on global health. Although estimates of current mortality are sensitive to the PM2.5 dataset (6.54 to 8.27 million/year using the Global Exposure Mortality Model), the projected near-term and long-term benefits of emissions reductions for reduced mortality are much more certain. Specifically, uncertainties in projected avoided deaths are consistently less than half the magnitude of uncertainties in recent mortality estimates. Under a low-emissions scenario, avoided cumulative deaths would exceed a quarter-billion by 2100.
Naturally-occurring colloids, particles of diameter < 10μm, are ubiquitous in geo-environments and can potentially facilitate transport of numerous contaminants in soil including heavy metals, pesticides, pathogens etc. via Colloid-Facilitated Transport (CFT). The CFT of contaminants to groundwater is still an underrepresented transport domain and may lead to significant environmental and health problems related to groundwater contamination. Colloid mobilization, transport and CFT in various geomedia are highly sensitive to physico-chemical perturbations. This study investigated colloid transport and colloid-facilitated heavy metal transport in saturated porous media with a series of column experiments using soil colloids extracted from two areas affected by Chronic Kidney Disease of Unknown Etiology (CKDu) in North Central Province of Sri Lanka. Colloid breakthrough curves were obtained from the column studies to observe the colloid transport under different flow rates (0.5±0.05, 1.65±0.05, 4.10±0.05 cm3/s) and ionic strengths (NaCl - 0.01 M, 0.05 M, 0.1 M). The CFT was studied using Cadmium (Cd(II)) as a model contaminant together with colloidal suspension under selected scenarios for high colloid deposition. Elevated colloid concentrations were observed in high CKDu affected area compared to the low endemic area. The experimental results were numerically simulated on an advection-diffusion/dispersion modelling framework coupled with first-order attachment, detachment and straining parameters inversely estimated using HYDRUS 1D software. Experimental and simulated colloid breakthrough curves showed a good agreement, and recognized colloid attachment as the key mechanism for colloid immobilization in selected soil. Both colloids and CFT of Cd(II) showed pronounced deposition under low flow rate and high ionic strength.
In Southern Thailand, Prince of Songkla University is the leading public university across five campuses, with Hat Yai being the main one. Science education is strong with several undergraduate programs; among them physics with geophysics electives where students can also do their term project. Groundwater exploration is among the main objectives of the Geophysics Research Center at PSU and undergraduate projects are well integrated into them. During their classes and laboratories, students learn the science behind groundwater and geophysical technologies. This requires working in the field and thus carrying heavy equipment. Female students, often the majority, have to do that in the same way as male students and thus learning it by doing it as a team. Teamwork is one of the essential social skills students learn. Undergraduate term projects in geophysics are always chosen based on a real problem, which is usually coming from outside the department and in most cases from outside the university, with customers ranging from local communities, over companies, to local government agencies. By this, the students learn the importance of their work for other people, thus giving them a motivation. Every project requires the students to initially talk with the customers to identify the problem and explore the area. They then have to decide on the geophysical survey plan with support from their advisor. After finishing the data acquisition and processing, the interpretation will be done in a team with a presentation, usually at the university. After that, students give a shorter presentation and report to the customers, explaining them in a non-scientific language their interpretations and conclusion. They also have to answer questions that came up. For doing this, in most cases the team is going back into the field where the customers are. This overall procedure with community engagement shows the student how important groundwater is in many rural areas and how difficult it can be to find groundwater, thus realizing them the importance of preserving it. If circumstance allow, later field teams might visit earlier projects done and learn about how the customers continued after the undergraduate project was finished. Photo: Student team engaged with local community leaders after a finished project, where a well was drilled.