How does the macroenvironment influence brain and behaviour-a review of current status and future perspectives.

The environment influences brain and mental health, both detrimentally and beneficially. Existing research has emphasised the individual psychosocial 'microenvironment'. Less attention has been paid to 'macroenvironmental' challenges, including climate change, pollution, urbanicity, and socioeconomic disparity. Notably, the implications of climate and pollution on brain and mental health have only recently gained prominence. With the advent of large-scale big-data cohorts and an increasingly dense mapping of macroenvironmental parameters, we are now in a position to characterise the relation between macroenvironment, brain, and behaviour across different geographic and cultural locations globally. This review synthesises findings from recent epidemiological and neuroimaging studies, aiming to provide a comprehensive overview of the existing evidence between the macroenvironment and the structure and functions of the brain, with a particular emphasis on its implications for mental illness. We discuss putative underlying mechanisms and address the most common exposures of the macroenvironment. Finally, we identify critical areas for future research to enhance our understanding of the aetiology of mental illness and to inform effective interventions for healthier environments and mental health promotion.

The influence of humid heat on morbidity of megacity Shanghai in China.

BACKGROUND: Increased attention has been paid to humid-heat extremes as they are projected to increase in both frequency and intensity. However, it remains unclear how compound extremes of heat and humidity affects morbidity when the climate is projected to continue warming in the future, in particular for a megacity with a large population. METHODS: We chose the Wet-Bulb Globe Temperature (WBGT) index as the metric to characterize the humid-heat exposure. The historical associations between daily outpatient visits and daily mean WBGT was established using a Distributed Lag Non-linear Model (DLNM) during the warm season (June to September) from 2013 to 2015 in Shanghai, a prominent megacity of China. Future morbidity burden related to the combined effect of high temperature and humidity were projected under four greenhouse gases (GHGs) emission scenarios (SSP126, SSP245, SSP370 and SSP585). RESULTS: The humid-heat weather was significantly associated with a higher risk of outpatient visits in Shanghai than the high-temperature conditions. Relative to the baseline period (2010-2019), the morbidity burden due to humid-heat weather was projected to increase 4.4 % (95 % confidence interval (CI): 1.1 %-10.1 %) even under the strict emission control scenario (SSP126) by 2100. Under the high-GHGs emission scenario (SSP585), this burden was projected to be 25.4 % (95 % CI: 15.8 %-38.4 %), which is 10.1 % (95 % CI: 6.5 %-15.8 %) more than that due to high-temperature weather. Our results also indicate that humid-hot nights could cause large morbidity risks under high-GHGs emission scenarios particularly in heat-sensible diseases such as the respiratory and cardiovascular disease by the end of this century. CONCLUSIONS: Humid heat exposures significantly increased the all-cause morbidity risk in the megacity Shanghai, especially in humid-hot nights. Our findings suggest that the combined effect of elevated temperature and humidity is projected to have more substantial impact on health compared to high temperature alone in a warming climate.

How does the macroenvironment influence brain and behaviour - a review of current status and future perspectives.

The environment influences mental health, both detrimentally and beneficially. Current research has emphasized the individual psychosocial 'microenvironment'. Less attention has been paid to 'macro-environmental' challenges including climate change, pollution, urbanicity and socioeconomic disparity. With the advent of large-scale big-data cohorts and an increasingly dense mapping of macroenvironmental parameters, we are now in a position to characterise the relation between macroenvironment, brain, and behaviour across different geographic and cultural locations globally. This review synthesises findings from recent epidemiological and neuroimaging studies, aiming to provide a comprehensive overview of the existing evidence between the macroenvironment and the structure and functions of the brain, with a particular emphasis on its implications for mental illness. We discuss putative underlying mechanisms and address the most common exposures of the macroenvironment. Finally, we identify critical areas for future research to enhance our understanding of the aetiology of mental illness and to inform effective interventions for healthier environments and mental health promotion.

City-level greenness exposure is associated with COVID-19 incidence in China.

Accumulating studies have suggested an important role of environmental factors (e.g. air pollutants) on the occurrence and development of coronavirus disease 2019 (COVID-19). Evidence concerning the relationship of greenness on COVID-19 is still limited. This study aimed to assess the association between greenness and COVID-19 incidence in 266 Chinese cities. A total of 12,377 confirmed COVID-19 cases were identified through February 29th, 2020. We used the average normalized difference vegetation index (NDVI) during January and February 2020 from MOD13A2 product, to represent the city-level greenness exposure. A generalized linear mixed-effects model was used to estimate the association between NDVI exposure and COVID-19 incidence using COVID-19 cases as the outcome. We evaluated whether the association was modified by population density, GDP per capita, and urbanization rate, and was mediated by air pollutants. We also performed a series of sensitivity analyses to discuss the robustness of our results. Per 0.1 unit increment in NDVI was negatively associated with COVID-19 incidence (IRR: 0.921, 95% CI: 0.898, 0.944) after adjustment for confounders. Associations with COVID-19 incidence were stronger in cities with lower population density, lower GDP per capita, and lower urbanization rate. We failed to detect any mediation effect of air pollutants on the association between NDVI and COVID-19 incidence. Sensitivity analyses also indicated consistent estimates. In conclusion, our study suggested a beneficial association between city-level greenness and COVID-19 incidence. We could not establish which mechanisms may explain this relationship.

Estimating a social cost of carbon for global energy consumption.

Estimates of global economic damage caused by carbon dioxide (CO2) emissions can inform climate policy1-3. The social cost of carbon (SCC) quantifies these damages by characterizing how additional CO2 emissions today impact future economic outcomes through altering the climate4-6. Previous estimates have suggested that large, warming-driven increases in energy expenditures could dominate the SCC7,8, but they rely on models9-11 that are spatially coarse and not tightly linked to data2,3,6,7,12,13. Here we show that the release of one ton of CO2 today is projected to reduce total future energy expenditures, with most estimates valued between -US$3 and -US$1, depending on discount rates. Our results are based on an architecture that integrates global data, econometrics and climate science to estimate local damages worldwide. Notably, we project that emerging economies in the tropics will dramatically increase electricity consumption owing to warming, which requires critical infrastructure planning. However, heating reductions in colder countries offset this increase globally. We estimate that 2099 annual global electricity consumption increases by about 4.5 exajoules (7 per cent of current global consumption) per one-degree-Celsius increase in global mean surface temperature (GMST), whereas direct consumption of other fuels declines by about 11.3 exajoules (7 per cent of current global consumption) per one-degree-Celsius increase in GMST. Our finding of net savings contradicts previous research7,8, because global data indicate that many populations will remain too poor for most of the twenty-first century to substantially increase energy consumption in response to warming. Importantly, damage estimates would differ if poorer populations were given greater weight14.

Global blue carbon accumulation in tidal wetlands increases with climate change.

Coastal tidal wetlands produce and accumulate significant amounts of organic carbon (C) that help to mitigate climate change. However, previous data limitations have prevented a robust evaluation of the global rates and mechanisms driving C accumulation. Here, we go beyond recent soil C stock estimates to reveal global tidal wetland C accumulation and predict changes under relative sea level rise, temperature and precipitation. We use data from literature study sites and our new observations spanning wide latitudinal gradients and 20 countries. Globally, tidal wetlands accumulate 53.65 (95%CI: 48.52-59.01) Tg C yr-1, which is ∼30% of the organic C buried on the ocean floor. Modeling based on current climatic drivers and under projected emissions scenarios revealed a net increase in the global C accumulation by 2100. This rapid increase is driven by sea level rise in tidal marshes, and higher temperature and precipitation in mangroves. Countries with large areas of coastal wetlands, like Indonesia and Mexico, are more susceptible to tidal wetland C losses under climate change, while regions such as Australia, Brazil, the USA and China will experience a significant C accumulation increase under all projected scenarios.

Emulating Ocean Dynamic Sea Level by Two-Layer Pattern Scaling.

Ocean dynamic sea level (DSL) change is a key driver of relative sea level (RSL) change. Projections of DSL change are generally obtained from simulations using atmosphere-ocean general circulation models (GCMs). Here, we develop a two-layer climate emulator to interpolate between emission scenarios simulated with GCMs and extend projections beyond the time horizon of available simulations. This emulator captures the evolution of DSL changes in corresponding GCMs, especially over middle and low latitudes. Compared with an emulator using univariate pattern scaling, the two-layer emulator more accurately reflects GCM behavior and captures non-linearities and non-stationarity in the relationship between DSL and global-mean warming, with a reduction in global-averaged error during 2271-2290 of 36%, 24%, and 34% in RCP2.6, RCP4.5, and RCP8.5, respectively. Using the emulator, we develop a probabilistic ensemble of DSL projections through 2300 for four scenarios: Representative Concentration Pathway (RCP) 2.6, RCP 4.5, RCP 8.5, and Shared Socioeconomic Pathway (SSP) 3-7.0. The magnitude and uncertainty of projected DSL changes decrease from the high-to the low-emission scenarios, indicating a reduced DSL rise hazard in low- and moderate-emission scenarios (RCP2.6 and RCP4.5) compared to the high-emission scenarios (SSP3-7.0 and RCP8.5).

Heatwaves, medications, and heat-related hospitalization in older Medicare beneficiaries with chronic conditions.

BACKGROUND: Heatwaves kill more people than floods, tornadoes, and earthquakes combined and disproportionally affect older persons and those with chronic conditions. Commonly used medications for chronic conditions, e.g., diuretics, antipsychotics disrupt thermoregulation or fluid/electrolyte balance and may sensitive patients to heat. However, the effect of heat-sensitizing medications and their interactions with heatwaves are not well-quantified. We evaluated effects of potentially heat-sensitizing medications in vulnerable older patients. METHODS: US Medicare data were linked at the zip code level to climate data with surface air temperatures for June-August of 2007-2012. Patients were Medicare beneficiaries aged ≥65 years with chronic conditions including diabetes, dementia, and cardiovascular, lung, or kidney disease. Exposures were potentially heat-sensitizing medications including diuretics, anticholinergics, antipsychotics, beta blockers, stimulants, and anti-hypertensives. A heatwave was defined as ≥2 days above the 95th percentile of historical zip code-specific surface air temperatures. We estimated associations of heat-sensitizing medications and heatwaves with heat-related hospitalization using self-controlled case series analysis. RESULTS: We identified 9,721 patients with at least one chronic condition and heat-related hospitalization; 42.1% of these patients experienced a heatwave. Heatwaves were associated with an increase in heat-related hospitalizations ranging from 21% (95% CI: 7% to 38%) to 33% (95% CI: 14% to 55%) across medication classes. Several drug classes were associated with moderately elevated risk of heat-related hospitalization in the absence of heatwaves, with rate ratios ranging from 1.16 (95% CI: 1.00 to 1.35) to 1.37 (95% CI: 1.14 to 1.66). We did not observe meaningful synergistic interactions between heatwaves and medications. CONCLUSIONS: Older patients with chronic conditions may be at heightened risk for heat-related hospitalization due to the use of heat-sensitizing medications throughout the summer months, even in the absence of heatwaves. Further studies are needed to confirm these findings and also to understand the effect of milder and shorter heat exposure.