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Environmental factors may operate differently when relations are measured across different geographical locations, a phenomenon known as spatial non-stationarity. This study investigates the spatial non-stationarity effect of unhealthy food environments and green spaces on the T2DM prevalence rate at the neighborhood level in Toronto. This study also compares how the results vary between age groups, classified as all adults (20 and above), young adults (from 20 to 44), middle adulthood (from 45 to 64), and seniors (65 and above). The geographically weighted regression model is utilized to explore the impacts of spatial non-stationarity effects on the research results, which may lead to biased conclusions, which have often been ignored in past studies. The results from this study reveal that environmental variables dissimilarly affect T2DM prevalence rates among different age groups and neighborhoods in Toronto after controlling for socioeconomic factors. For example, the green space density yields positive associations with diabetes prevalence rates for elder generations but negative relationships for younger age groups in twenty-two and four neighborhoods, respectively, around Toronto East. The observed associations will provide beneficial suggestions to support government and public health authorities in designing education, prevention, and intervention programs targeting different neighborhoods to control the burden of diabetes.

Sufficient production, consistent food supply, and environmental protection in urban +settings are major global concerns for future sustainable cities. Currently, sustainable food supply is under intense pressure due to exponential population growth, expanding urban dwellings, climate change, and limited natural resources. The recent novel coronavirus 2019 (COVID-19) pandemic crisis has impacted sustainable fresh food supply, and has disrupted the food supply chain and prices significantly. Under these circumstances, urban horticulture and crop cultivation have emerged as potential ways to expand to new locations through urban green infrastructure. Therefore, the objective of this study is to review the salient features of contemporary urban horticulture, in addition to illustrating traditional and innovative developments occurring in urban environments. Current urban cropping systems, such as home gardening, community gardens, edible landscape, and indoor planting systems, can be enhanced with new techniques, such as vertical gardening, hydroponics, aeroponics, aquaponics, and rooftop gardening. These modern techniques are ecofriendly, energy- saving, and promise food security through steady supplies of fresh fruits and vegetables to urban neighborhoods. There is a need, in this modern era, to integrate information technology tools in urban horticulture, which could help in maintaining consistent food supply during (and after) a pandemic, as well as make agriculture more sustainable.

The effect of extreme temperature has become an increasing public health concern. Evaluating the impact of ambient temperature on morbidity has received less attention than its impact on mortality.We performed a systematic literature review and extracted quantitative estimates of the effects of hot temperatures on cardiorespiratory morbidity. There were too few studies on effects of cold temperatures to warrant a summary. Pooled estimates of effects of heat were calculated using a Bayesian hierarchical approach that allowed multiple results to be included from the same study, particularly results at different latitudes and with varying lagged effects.Twenty-one studies were included in the final meta-analysis. The pooled results suggest an increase of 3.2% (95% posterior interval = -3.2% to 10.1%) in respiratory morbidity with 1°C increase on hot days. No apparent association was observed for cardiovascular morbidity (-0.5% [-3.0% to 2.1%]). The length of lags had inconsistent effects on the risk of respiratory and cardiovascular morbidity, whereas latitude had little effect on either.The effects of temperature on cardiorespiratory morbidity seemed to be smaller and more variable than previous findings related to mortality.

Many countries are developing or reviewing national adaptation policy for climate change but the extent to which these meet the health needs of vulnerable groups has not been assessed. This study examines the adequacy of such policies for nine known climate-vulnerable groups: people with mental health conditions, Aboriginal people, culturally and linguistically diverse groups, aged people, people with disabilities, rural communities, children, women, and socioeconomically disadvantaged people.The study analyses an exhaustive sample of national adaptation policy documents from Annex 1 ('developed') countries of the United Nations Framework Convention on Climate Change: 20 documents from 12 countries. A 'critical computational linguistics' method was used involving novel software-driven quantitative mapping and traditional critical discourse analysis.The study finds that references to vulnerable groups are relatively little present or non-existent, as well as poorly connected to language about practical strategies and socio-economic contexts, both also little present.The conclusions offer strategies for developing policy that is better informed by a 'social determinants of health' definition of climate vulnerability, consistent with best practice in the literature and global policy prescriptions.

Miniaturization of electrochemical detection ofPseudomonas aeruginosaquorum sensing molecules at a liquid/liquid interface through facilitated proton transfer reactions.

Abstract Climate change is already impacting the North American Great Lakes ecosystem and understanding the relationship between climate events and public health, such as waterborne acute gastrointestinal illnesses (AGIs), can help inform needed adaptive capacity for drinking water systems (DWSs). In this study, we assessed a harmonized binational dataset for the effects of extreme precipitation events (≥90th percentile) and preceding dry periods, source water turbidity, total coliforms, and protozoan AGIs – cryptosporidiosis and giardiasis – in the populations served by four DWSs that source surface water from Lake Ontario (Hamilton and Toronto, Ontario, Canada) and Lake Michigan (Green Bay and Milwaukee, Wisconsin, USA) from January 2009 through August 2014. We used distributed lag non-linear Poisson regression models adjusted for seasonality and found extreme precipitation weeks preceded by dry periods increased the relative risk of protozoan AGI after 1 and 3–5 weeks in three of the four cities, although only statistically significant in two. Our results suggest that the risk of protozoan AGI increases with extreme precipitation preceded by a dry period. As extreme precipitation patterns become more frequent with climate change, the ability to detect changes in water quality and effectively treat source water of varying quality is increasingly important for adaptive capacity and protection of public health.

Air pollution is a major issue all over the world because of its impacts on the environment and human beings. The present review discussed the sources and impacts of pollutants on environmental and human health and the current research status on environmental pollution forecasting techniques in detail; this study presents a detailed discussion of the Artificial Intelligence methodologies and Machine learning (ML) algorithms used in environmental pollution forecasting and early-warning systems; moreover, the present work emphasizes more on Artificial Intelligence techniques (particularly Hybrid models) used for forecasting various major pollutants (e.g., PM2.5, PM10, O3, CO, SO2, NO2, CO2) in detail; moreover, focus is given to AI and ML techniques in predicting chronic airway diseases and the prediction of climate changes and heat waves. The hybrid model has better performance than single AI models and it has greater accuracy in prediction and warning systems. The performance evaluation error indexes like R2, RMSE, MAE and MAPE were highlighted in this study based on the performance of various AI models.

West and East Africa experience high vari- ability of rainfall that is expected to increase with climate change. This results in fluctuations in water availability for food production and other socioeconomic activities. Water harvesting and storage can mitigate the adverse effects of rainfall variability. But past studies have shown that when investments in water storage are not guided by environ- mental health considerations, the increased availability of open water surface may increase the transmission of water- related diseases. This is demonstrated for schistosomiasis associated with small reservoirs in Burkina Faso, and for malaria in Ethiopia around large dams, small dams, and water harvesting ponds. The concern is that the rush to develop water harvesting and storage for climate change adaptation may increase the risk for already vulnerable people, in some cases more than canceling out the benefits of greater water availability. Taking health issues into account in a participatory approach to planning, design, and management of rainwater harvesting and water storage, as well as considering the full range of water storage options would enable better opportunities for enhancing resilience against climate change in vulnerable populations in sub-Saharan Africa.