Suitability of maize crop residue fermented by Pleurotus ostreatus as feed for edible crickets: growth performance, micronutrient content, and iron bioavailability.

Small-scale farming of edible insects could help combat public health challenges such as protein energy malnutrition and anemia, but reliable low-cost feeds for insects are needed. In resource-limited contexts, where grains such as maize are prohibitively costly for use as insect feed, the feasibility of insect farming may depend on finding alternatives. Here, we explore the potential to modify plentiful maize crop residue with edible mushroom mycelium to generate a low-cost feed adjunct for the farmed two-spotted cricket, Gryllus bimaculatus. Mushroom farming, like insect agriculture, is versatile; it can yield nutritious food while increasing system circularity by utilizing lignocellulosic residues from row crops as inputs. Pleurotus ostreatus, is an edible basidiomycete capable of being cultivated on corn stover (Zea mays). Mushroom harvest results in abundant "spent" substrate, which we investigated as a candidate feed ingredient. We created six cricket feeds containing fermented Pleurotus substrate plus an unfermented control, measuring cricket mass, mortality, and maturation weekly to evaluate cricket growth performance impacts of both fungal fermentation duration and mushroom formation. Pasteurized corn stover was inoculated with P. ostreatus mycelium and fermented for 0, 2, 3, 4, or 8 weeks. Some 4 and 8-week substrates were induced to produce mushrooms through manipulations of temperature, humidity, and light conditions. Dried fermented stover (40%) was added to a 1:1 corn/soy grain mix and fed to crickets ad libitum for 44 days. The unfermented control group showed higher survivorship compared to several fermented diets. Control group mass yield was higher for 2 out of 6 fermented diets. Little variation in cricket iron content was observed via ICP-spectrometry across feeds, averaging 2.46 mg/100 g. To determine bioavailability, we conducted in vitro Caco-2 human colon epithelial cell absorption assays, showing that iron in crickets fed fruiting-induced substrates was more bioavailable than in unfruited groups. Despite more bioavailable iron in crickets reared on post-fruiting substrates, we conclude that Pleurotus-fermented stover is an unsuitable feed ingredient for G. bimaculatus due to high mortality, variability in growth responses within treatments, and low mass yield.

Over half of known human pathogenic diseases can be aggravated by climate change.

It is relatively well accepted that climate change can affect human pathogenic diseases; however, the full extent of this risk remains poorly quantified. Here we carried out a systematic search for empirical examples about the impacts of ten climatic hazards sensitive to greenhouse gas (GHG) emissions on each known human pathogenic disease. We found that 58% (that is, 218 out of 375) of infectious diseases confronted by humanity worldwide have been at some point aggravated by climatic hazards; 16% were at times diminished. Empirical cases revealed 1,006 unique pathways in which climatic hazards, via different transmission types, led to pathogenic diseases. The human pathogenic diseases and transmission pathways aggravated by climatic hazards are too numerous for comprehensive societal adaptations, highlighting the urgent need to work at the source of the problem: reducing GHG emissions.

Nationwide and Regional PM2.5-Related Air Quality Health Benefits From the Removal of Energy-Related Emissions in the United States.

Clean energy policy can provide substantial health benefits through improved air quality. As ambitious clean energy proposals are increasingly considered and adopted across the United States (US), quantifying the benefits of removal of such large air pollution emissions sources is crucial to understanding potential societal impacts of such policy. In this study, we estimate health benefits resulting from the elimination of emissions of fine particulate matter (PM2.5), sulfur dioxide, and nitrogen oxides from the electric power, transportation, building, and industrial sectors in the contiguous US. We use EPA's CO-Benefits Risk Assessment screening tool to estimate health benefits resulting from the removal of PM2.5-related emissions from these energy-related sectors. We find that nationwide efforts to eliminate energy-related emissions could prevent 53,200 (95% CI: 46,900-59,400) premature deaths each year and provide $608 billion ($537-$678 billion) in benefits from avoided PM2.5-related illness and death. We also find that an average of 69% (range: 32%-95%) of the health benefits from emissions removal remain in the emitting region. Our study provides an indication of the potential scale and distribution of public health benefits that could result from ambitious regional and nationwide clean energy and climate mitigation policy.

Climate Solutions Double as Health Interventions.

The climate crisis threatens to exacerbate numerous climate-sensitive health risks, including heatwave mortality, malnutrition from reduced crop yields, water- and vector-borne infectious diseases, and respiratory illness from smog, ozone, allergenic pollen, and wildfires. Recent reports from the Intergovernmental Panel on Climate Change stress the urgent need for action to mitigate climate change, underscoring the need for more scientific assessment of the benefits of climate action for health and wellbeing. Project Drawdown has analyzed more than 80 solutions to address climate change, building on existing technologies and practices, that could be scaled to collectively limit warming to between 1.5° and 2 °C above preindustrial levels. The solutions span nine major sectors and are aggregated into three groups: reducing the sources of emissions, maintaining and enhancing carbon sinks, and addressing social inequities. Here we present an overview of how climate solutions in these three areas can benefit human health through improved air quality, increased physical activity, healthier diets, reduced risk of infectious disease, and improved sexual and reproductive health, and universal education. We find that the health benefits of a low-carbon society are more substantial and more numerous than previously realized and should be central to policies addressing climate change. Much of the existing literature focuses on health effects in high-income countries, however, and more research is needed on health and equity implications of climate solutions, especially in the Global South. We conclude that adding the myriad health benefits across multiple climate change solutions can likely add impetus to move climate policies faster and further.

Source Apportionment of Fine Organic Particulate Matter (PM2.5) in Central Addis Ababa, Ethiopia.

The development of infrastructure, a rapidly increasing population, and urbanization has resulted in increasing air pollution levels in the African city of Addis Ababa. Prior investigations into air pollution have not yet sufficiently addressed the sources of atmospheric particulate matter. This study aims to identify the major sources of fine particulate matter (PM2.5) and its seasonal contribution in Addis Ababa, Ethiopia. Twenty-four-hour average PM2.5 mass samples were collected every 6th day, from November 2015 through November 2016. Chemical species were measured in samples and source apportionment was conducted using a chemical mass balance (CMB) receptor model that uses particle-phase organic tracer concentrations to estimate source contributions to PM2.5 organic carbon (OC) and the overall PM2.5 mass. Vehicular sources (28%), biomass burning (18.3%), plus soil dust (17.4%) comprise about two-thirds of the PM2.5 mass, followed by sulfate (6.5%). The sources of air pollution vary seasonally, particularly during the main wet season (June-September) and short rain season (February-April): From motor vehicles, (31.0 ± 2.6%) vs. (24.7 ± 1.2%); biomass burning, (21.5 ± 5%) vs. (14 ± 2%); and soil dust, (11 ± 6.4%) vs. (22.7 ± 8.4%), respectively, are amongst the three principal sources of ambient PM2.5 mass in the city. We suggest policy measures focusing on transportation, cleaner fuel or energy, waste management, and increasing awareness on the impact of air pollution on the public's health.

Fine particulate pollution concentration in Addis Ababa exceeds the WHO guideline value: Results of 3 years of continuous monitoring and health impact assessment.

Real-time monitoring of fine particulate matter (PM2.5) concentrations and assessing the health impact are limited in Ethiopia. The objective of this study is to describe current levels of PM2.5 air pollution in Addis Ababa and examine temporal patterns and to consider the health impact of current PM2.5 exposure levels. METHODS: PM2.5 concentrations were measured using a centrally-located Beta Attenuator Monitor (BAM-1022) for 3 years (1 April 2017 to 31 March 2020), with data downloaded biweekly. Deaths attributable to current PM2.5 concentration levels were estimated using the AirQ+ tool. The daily average was estimated using hourly data. RESULTS: The daily mean (SD) PM2.5 concentration was 42.4 µg/m3 (15.98). Two daily extremes were observed: morning (high) and afternoon (low). Sundays had the lowest PM2.5 concentration, while Mondays to Thursdays saw a continuous increase; Fridays showed the highest concentration. Seasons showed marked variation, with the highest values during the wet season. Concentration spikes reflected periods of intensive fuel combustion. A total of 502 deaths (4.44%) were attributable to current air pollution levels referenced to the 35 µg/m3 WHO interim target annual level and 2,043 (17.7%) at the WHO 10 µg/m3 annual guideline. CONCLUSION: PM2.5 daily levels were 1.7 times higher than the WHO-recommended 24-hour guideline. The current annual mean PM2.5 concentration results in a substantial burden of attributable deaths compared to an annual mean of 10 µg/m3. The high PM2.5 level and its variability across days and seasons calls for citywide interventions to promote clean air.

Intracounty modeling of COVID-19 infection with human mobility: Assessing spatial heterogeneity with business traffic, age, and race.

The COVID-19 pandemic is a global threat presenting health, economic, and social challenges that continue to escalate. Metapopulation epidemic modeling studies in the susceptible-exposed-infectious-removed (SEIR) style have played important roles in informing public health policy making to mitigate the spread of COVID-19. These models typically rely on a key assumption on the homogeneity of the population. This assumption certainly cannot be expected to hold true in real situations; various geographic, socioeconomic, and cultural environments affect the behaviors that drive the spread of COVID-19 in different communities. What's more, variation of intracounty environments creates spatial heterogeneity of transmission in different regions. To address this issue, we develop a human mobility flow-augmented stochastic SEIR-style epidemic modeling framework with the ability to distinguish different regions and their corresponding behaviors. This modeling framework is then combined with data assimilation and machine learning techniques to reconstruct the historical growth trajectories of COVID-19 confirmed cases in two counties in Wisconsin. The associations between the spread of COVID-19 and business foot traffic, race and ethnicity, and age structure are then investigated. The results reveal that, in a college town (Dane County), the most important heterogeneity is age structure, while, in a large city area (Milwaukee County), racial and ethnic heterogeneity becomes more apparent. Scenario studies further indicate a strong response of the spread rate to various reopening policies, which suggests that policy makers may need to take these heterogeneities into account very carefully when designing policies for mitigating the ongoing spread of COVID-19 and reopening.

Improving Human Health in China Through Alternative Energy.

In this study, we estimate the health benefits of more stringent alternative energy goals and the costs of reducing coal-fired power plant pollution in China projected in 2030. One of our two overarching alternative energy goals was to estimate the health benefits of complete elimination of coal energy, supplemented by natural gas and renewables. The second was a policy scenario similar to the U.S. 2013 Climate Action Plan (CAP), which played a pivotal role leading up to the 2015 Paris Climate Agreement. We used the Greenhouse Gas and Air Pollution Interactions and Synergies (GAINS) model created by the International Institute for Applied Systems Analysis for our model simulations. We found that 17,137-24,220 premature deaths can be avoided if coal energy is completely replaced by alternative energy, and 8,693-9,281 premature deaths can be avoided if coal energy is replaced by alternatives in a CAP-like scenario. A CAP-like scenario using emission-controls in coal plants costs $11-18 per person. Reducing coal energy in China under a CAP-like scenario would free up $9.4 billion in the annual energy budget to spend on alternatives, whereas eliminating the cost of coal energy frees up $32 billion. This study's estimates show that more stringent alternative energy targets in China are worth the investment in terms of health.

Land use-induced spillover: a call to action to safeguard environmental, animal, and human health.

The rapid global spread and human health impacts of SARS-CoV-2, the virus that causes COVID-19, show humanity's vulnerability to zoonotic disease pandemics. Although anthropogenic land use change is known to be the major driver of zoonotic pathogen spillover from wildlife to human populations, the scientific underpinnings of land use-induced zoonotic spillover have rarely been investigated from the landscape perspective. We call for interdisciplinary collaborations to advance knowledge on land use implications for zoonotic disease emergence with a view toward informing the decisions needed to protect human health. In particular, we urge a mechanistic focus on the zoonotic pathogen infect-shed-spill-spread cascade to enable protection of landscape immunity-the ecological conditions that reduce the risk of pathogen spillover from reservoir hosts-as a conservation and biosecurity priority. Results are urgently needed to formulate an integrated, holistic set of science-based policy and management measures that effectively and cost-efficiently minimise zoonotic disease risk. We consider opportunities to better institute the necessary scientific collaboration, address primary technical challenges, and advance policy and management issues that warrant particular attention to effectively address health security from local to global scales.

Developing A Definition Of Climate And Health Literacy.

A new generation of activists is calling for bold responses to the climate crisis. Although young people are motivated to act on climate issues, existing educational frameworks do not adequately prepare them by addressing the scope and complexity of the human health risks associated with climate change. We adapted the US government's climate literacy principles to propose a definition and corresponding set of elements for a concept we term climate and health literacy. We conducted a scoping review to assess how the peer-reviewed literature addresses these elements. Our analysis reveals a focus on training health professionals; more international than US domestic content; and limited information about data and models, fossil fuels, and equity. We propose developing a framework that builds on the elements to support a broader educational agenda that prepares students and future leaders to recognize the complex health ramifications of a changing climate.

Chemical Characterization and Seasonality of Ambient Particles (PM2.5) in the City Centre of Addis Ababa.

Ambient air pollution is a growing public health concern in major African cities, including Addis Ababa (Ethiopia), where little information is available on fine particulate matter (PM2.5, with aerodynamic diameter <2.5 µm) pollution. This paper aims to characterize annual PM2.5, including bulk composition and seasonal patterns, in Addis Ababa. We collected 24-h PM2.5 samples in the central city every 6 days from November 2015 to November 2016. The mean (±SD) daily PM2.5 concentration was 53.8 (±25.0) µg/m3, with 90% of sampled days exceeding the World Health Organization's guidelines. Principal components were organic matter (OM, 44.5%), elemental carbon (EC, 25.4%), soil dust (13.5%), and SNA (sulfate, nitrate, and ammonium ions, 8.2%). Higher PM2.5 concentrations were observed during the heavy rain season, while crustal dust concentrations ranged from 2.9 to 37.6%, with higher levels during dry months. Meteorological variables, vehicle emissions, biomass fuels, unpaved roads, and construction activity contribute to poor air quality. Compared to the Air Quality Index (AQI), 31% and 36% of observed days were unhealthy for everyone and unhealthy for sensitive groups, respectively. We recommend adopting effective prevention strategies and pursuing research on vehicle emissions, biomass burning, and dust control to curb air pollution in the city.

Association of Mobile Phone Location Data Indications of Travel and Stay-at-Home Mandates With COVID-19 Infection Rates in the US.

Importance: A stay-at-home social distancing mandate is a key nonpharmacological measure to reduce the transmission rate of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), but a high rate of adherence is needed. Objective: To examine the association between the rate of human mobility changes and the rate of confirmed cases of SARS-CoV-2 infection. Design, Setting, and Participants: This cross-sectional study used daily travel distance and home dwell time derived from millions of anonymous mobile phone location data from March 11 to April 10, 2020, provided by the Descartes Labs and SafeGraph to quantify the degree to which social distancing mandates were followed in the 50 US states and District of Columbia and the association of mobility changes with rates of coronavirus disease 2019 (COVID-19) cases. Exposure: State-level stay-at-home orders during the COVID-19 pandemic. Main Outcomes and Measures: The main outcome was the association of state-specific rates of COVID-19 confirmed cases with the change rates of median travel distance and median home dwell time of anonymous mobile phone users. The increase rates are measured by the exponent in curve fitting of the COVID-19 cumulative confirmed cases, while the mobility change (increase or decrease) rates were measured by the slope coefficient in curve fitting of median travel distance and median home dwell time for each state. Results: Data from more than 45 million anonymous mobile phone devices were analyzed. The correlation between the COVID-19 increase rate and travel distance decrease rate was -0.586 (95% CI, -0.742 to -0.370) and the correlation between COVID-19 increase rate and home dwell time increase rate was 0.526 (95% CI, 0.293 to 0.700). Increases in state-specific doubling time of total cases ranged from 1.0 to 6.9 days (median [interquartile range], 2.7 [2.3-3.3] days) before stay-at-home orders were enacted to 3.7 to 30.3 days (median [interquartile range], 6.0 [4.8-7.1] days) after stay-at-home social distancing orders were put in place, consistent with pandemic modeling results. Conclusions and Relevance: These findings suggest that stay-at-home social distancing mandates, when they were followed by measurable mobility changes, were associated with reduction in COVID-19 spread. These results come at a particularly critical period when US states are beginning to relax social distancing policies and reopen their economies. These findings support the efficacy of social distancing and could help inform future implementation of social distancing policies should they need to be reinstated during later periods of COVID-19 reemergence.

Using Satellites to Track Indicators of Global Air Pollution and Climate Change Impacts: Lessons Learned From a NASA-Supported Science-Stakeholder Collaborative.

The 2018 NASA Health and Air Quality Applied Science Team (HAQAST) "Indicators" Tiger Team collaboration between NASA-supported scientists and civil society stakeholders aimed to develop satellite-derived global air pollution and climate indicators. This Commentary shares our experience and lessons learned. Together, the team developed methods to track wildfires, dust storms, pollen counts, urban green space, nitrogen dioxide concentrations and asthma burdens, tropospheric ozone concentrations, and urban particulate matter mortality. Participatory knowledge production can lead to more actionable information but requires time, flexibility, and continuous engagement. Ground measurements are still needed for ground truthing, and sustained collaboration over time remains a challenge.

A 15 Year Evaluation of West Nile Virus in Wisconsin: Effects on Wildlife and Human Health.

West Nile virus (WNV) is the most important and widespread mosquito-borne virus in the United States (U.S.). WNV has the ability to spread rapidly and effectively, infecting more than 320 bird and mammalian species. An examination of environmental conditions and the health of keystone species may help predict the susceptibility of various habitats to WNV and reveal key risk factors, annual trends, and vulnerable regions. Since 2002, WNV outbreaks in Wisconsin varied by species, place, and time, significantly affected by unique climatic, environmental, and geographical factors. During a 15 year period, WNV was detected in 71 of 72 counties, resulting in 239 human and 1397 wildlife cases. Controlling for population and sampling efforts in Wisconsin, rates of WNV are highest in the western and northwestern rural regions of the state. WNV incidence rates were highest in counties with low human population densities, predominantly wetland, and at elevations greater than 1000 feet. Resources for surveillance, prevention, and detection of WNV were lowest in rural counties, likely resulting in underestimation of cases. Overall, increasing mean temperature and decreasing precipitation showed positive influence on WNV transmission in Wisconsin. This study incorporates the first statewide assessment of WNV in Wisconsin.

Seasonal patterns of dengue fever in rural Ecuador: 2009-2016.

Season is a major determinant of infectious disease rates, including arboviruses spread by mosquitoes, such as dengue, chikungunya, and Zika. Seasonal patterns of disease are driven by a combination of climatic or environmental factors, such as temperature or rainfall, and human behavioral time trends, such as school year schedules, holidays, and weekday-weekend patterns. These factors affect both disease rates and healthcare-seeking behavior. Seasonality of dengue fever has been studied in the context of climatic factors, but short- and long-term time trends are less well-understood. With 2009-2016 medical record data from patients diagnosed with dengue fever at two hospitals in rural Ecuador, we used Poisson generalized linear modeling to determine short- and long-term seasonal patterns of dengue fever, as well as the effect of day of the week and public holidays. In a subset analysis, we determined the impact of school schedules on school-aged children. With a separate model, we examined the effect of climate on diagnosis patterns. In the first model, the most important predictors of dengue fever were annual sinusoidal fluctuations in disease, long-term trends (as represented by a spline for the full study duration), day of the week, and hospital. Seasonal trends showed single peaks in case diagnoses, during mid-March. Compared to the average of all days, cases were more likely to be diagnosed on Tuesdays (risk ratio (RR): 1.26, 95% confidence interval (CI) 1.05-1.51) and Thursdays (RR: 1.25, 95% CI 1.02-1.53), and less likely to be diagnosed on Saturdays (RR: 0.81, 95% CI 0.65-1.01) and Sundays (RR: 0.74, 95% CI 0.58-0.95). Public holidays were not significant predictors of dengue fever diagnoses, except for an increase in diagnoses on the day after Christmas (RR: 2.77, 95% CI 1.46-5.24). School schedules did not impact dengue diagnoses in school-aged children. In the climate model, important climate variables included the monthly total precipitation, an interaction between total precipitation and monthly absolute minimum temperature, an interaction between total precipitation and monthly precipitation days, and a three-way interaction between minimum temperature, total precipitation, and precipitation days. This is the first report of long-term dengue fever seasonality in Ecuador, one of few reports from rural patients, and one of very few studies utilizing daily disease reports. These results can inform local disease prevention efforts, public health planning, as well as global and regional models of dengue fever trends.