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Communities in the Pamir Mountains of Central Asia are among the most vulnerable to climate change due to their geographic location and subsistence-based livelihoods. Historically, ecological calendars supported their agropastoral lifestyles which provided anticipatory capacity to seasonal changes. Due to decades of Soviet colonization and socioecological transformations, knowledge of these ecological calendars fell into disuse. In 2016, Savnob and Roshorv, two villages in the Bartang Valley of Tajikistan, began the revitalization of these calendars using a participatory action research process through knowledge co-generation. We undertook a comparative analysis to investigate the importance of context-specificity to ensure food security and reduce their vulnerability to climate change. A preliminary analysis of the temperature regime and local language terms, relating to the positioning and quality of land, framed our methods-of-analysis. We compared the villagers' ecological calendars by focusing on indicator species, potentially threatening weather events, land-use, livelihood activities, and the role of the vernal equinox. Despite their close geographic proximity, context-specificity determined by distinct microecologies influences the timing and practice of these communities' livelihood activities. These villages have different dependencies on biotic and abiotic events, crops, and land-use; all of which affect food security and survival. These differences contributed to mutual support between the two villages, increased the availability of food, and thereby, lowered their vulnerability to climate change. As Savnob's and Roshorv's ecological calendars are updated with changing climate, they can once again enhance their anticipatory capacity while reducing their vulnerability.

Acute exacerbation of chronic obstruction pulmonary disease (AECOPD) as a respiratory disease, is considered to be related to air pollution by more and more studies. However, the evidence on how air pollution affect the incidence of AECOPD and whether there are population differences is still insufficient. Therefore, we select PM10, PM2.5, SO2, NO2, CO, and O3 as representatives combined with daily AECOPD admission data from 1 January 2015 to 26 June 2016 in the rural areas of Qingyang, northwestern China to explore the associations of air pollution with AECOPD. Based on a time‐stratified case‐crossover design, we constructed a distributed lag nonlinear model to qualify the single and cumulative lagged effects of air pollution on AECOPD. Stratified related risks by sex and age were also reported. The cumulative exposure‐response curves were approximately linear for PM2.5, “V”‐shaped for PM10, “U”‐shaped for NO2 and inverted‐“V” for SO2, CO and O3. Exposure to high‐PM2.5 (42 μg/m3), high‐PM10 (91 μg/m3), high‐SO2 (58 μg/m3), low‐NO2 (12 μg/m3), and high‐CO (1.55 mg/m3) increased the risk of AECOPD. Females aged 15–64 were more susceptible under extreme concentrations of PM2.5, SO2, CO, and low‐PM10 than other subgroups. In addition, adults aged 15–64 were more sensitive to extreme concentrations of NO2 compared with the elderly ≥65 years old, while the latter were more sensitive to high‐PM10. High‐SO2, high‐NO2, and extreme concentrations of PM2.5 had the greatest effects on the day of exposure, while low‐SO2 and low‐CO had lagged effects on AECOPD. Precautionary measures should be taken with a focus on vulnerable subgroups, to control hospitalization for AECOPD associated with air pollutants., Key Points Exposure to high‐PM2.5, high‐PM10, high‐SO2, low‐NO2, and high‐CO increased the risk of acute exacerbation of chronic obstruction pulmonary disease (COPD)The cumulative curves were approximately linear for PM2.5, “V”‐shaped for PM10, “U”‐shaped for NO2 and inverted‐”V” for SO2, CO and O3 The nonlinear effects on acute exacerbation of COPD at different lags varied based on the air pollutants, involved gender and age

Accelerated urbanization increases both the frequency and intensity of heatwaves (HW) and urban heat islands (UHIs). An extreme HW event occurred in 2012 summer that caused temperatures of more than 40°C in Chicago, Illinois, USA, which is a highly urbanized city impacted by UHIs. In this study, multiple numerical models, including the High Resolution Land Data Assimilation System (HRLDAS) and Weather Research and Forecasting (WRF) model, were used to simulate the HW and UHI, and their performance was evaluated. In addition, sensitivity testing of three different WRF configurations was done to determine the impact of increasing model complexity in simulating urban meteorology. Model performances were evaluated based on the statistical performance metrics, the application of a multi‐layer urban canopy model (MLUCM) helps WRF to provide the best performance in this study. HW caused rural temperatures to increase by ∼4°C, whereas urban Chicago had lower magnitude increases from the HW (∼2–3°C increases). Nighttime UHI intensity (UHII) ranged from 1.44 to 2.83°C during the study period. Spatiotemporal temperature fields were used to estimate the potential heat‐related exposure and to quantify the Excessive Heat Factor (EHF). The EHF during the HW episode provides a risk map indicating that while urban Chicago had higher heat‐related stress during this event, the rural area also had high risk, especially during nighttime in central Illinois. This study provides a reliable method to estimate spatiotemporal exposures for future studies of heat‐related health impacts., Key Points Extreme heatwave induced ∼4°C increase in temperature in rural Chicago while increased urban temperature by 2–3°CUrban heat island intensity is estimated to be around 1.44–2.38°CExcessive Heat Factor is higher than 50°C2 in urban Chicago due to heatwave

The Gulf of Mexico Research Initiative (GoMRI) was established in 2010 with $500 million in funding provided by British Petroleum over a 10‐year period to support research on the impacts of the Deepwater Horizon oil spill and recovery. Contributions of the GoMRI program to date focused on human health are presented in more than 32 peer‐reviewed papers published between 2011 and May 2019. Primary findings from review of these papers are (i) the large quantity of dispersants used in the oil cleanup have been associated with human health concerns, including through obesogenicity, toxicity, and illnesses from aerosolization of the agents; (ii) oil contamination has been associated with potential for increases in harmful algal blooms and numbers of pathogenic Vibrio bacteria in oil‐impacted waters; and (iii) members of Gulf communities who are heavily reliant upon natural resources for their livelihoods were found to be vulnerable to high levels of life disruptions and institutional distrust. Positive correlations include a finding that a high level of community attachment was beneficial for recovery. Actions taken to improve disaster response and reduce stress‐associated health effects could lessen negative impacts of similar disasters in the future. Furthermore, GoMRI has supported annual conferences beginning in 2013 at which informative human health‐related presentations have been made. Based on this review, it is recommended that the Oil Pollution Act of 1990 be updated to include enhanced funding for oil spill impacts to human health., Key Points Nearly 3% of >1,200 Gulf of Mexico Research Initiative publications were directly related to human health, a higher proportion than previous oil spill researchDispersants contained possible obesogens and decreased aerosolized volatile organic compound exposure but increased particulate matter concentrationsThose who rely on natural resources are more vulnerable to oil spills and cleanup methods that may increase exposure to harmful chemicals, algae, and bacteria

Recurrent and large forest fires negatively impact ecosystem, air quality, and human health. Moderate Resolution Imaging Spectroradiometer fire product is used to identify forest fires over central India domain, an extremely fire prone region. The study finds that from 2001 to 2020, ∼70% of yearly forest fires over the region occurred during March (1,857.5 counts/month) and April (922.8 counts/month). Some years such as 2009, 2012, and 2017 show anomalously high forest fires. The role of persistent warmer temperatures and multiple climate extremes in increasing forest fire activity over central India is comprehensively investigated. Warmer period from 2006 to 2020 showed doubling and tripling of forest fire activity during forest fire (February–June; FMAMJ) and non‐fire (July–January; JASONDJ) seasons, respectively. From 2015 JASONDJ to 2018 FMAMJ, central India experienced a severe heatwave, a rare drought and an extremely strong El Niño, the combined effect of which is linked to increased forest fires. Further, the study assesses quinquennial spatiotemporal changes in forest fire characteristics such as fire count density and average fire intensity. Deciduous forests of Jagdalpur‐Gadchiroli Range and Indravati National Park in Chhattisgarh state are particularly fire prone (>61 fire counts/grid) during FMAMJ and many forest fires are of high intensity (>45 MW). Statistical associations link high near surface air temperature and low precipitation during FMAMJ to significantly high soil temperature, low soil moisture content, low evapotranspiration and low normalized difference vegetation index. This creates a significantly drier environment, conducive for high forest fire activity in the region., Key Points Compared to 2001–2005, central India domain forest fire activity during 2006–2020 doubled in forest fire season and tripled in non‐fire seasonRole of persistent warmer temperatures and multiple climate extremes in increasing forest fire activity over central India is highlightedDeciduous forests of Jagdalpur‐Gadchiroli Range and Indravati National Park are extremely fire prone and fires are of high intensity

The PM2.5 exposure risk assessment is the foundation to reduce its adverse effects. Population survey‐related data have been deficient in high spatiotemporal detailed descriptions. Social media data can quantify the PM2.5 exposure risk at high spatiotemporal resolutions. However, due to the no‐sample characteristics of social media data, PM2.5 exposure risk for older adults is absent. We proposed combining social media data and population survey‐derived data to map the total PM2.5 exposure risk. Hourly exceedance PM2.5 exposure risk indicators based on population modeling (HEPEpmd) and social media data (HEPEsm) were developed. Daily accumulative HEPEsm and HEPEpsd ranged from 0 to 0.009 and 0 to 0.026, respectively. Three peaks of HEPEsm and HEPEpsd were observed at 13:00, 18:00, and 22:00. The peak value of HEPEsm increased with time, which exhibited a reverse trend to HEPEpsd. The spatial center of HEPEsm moved from the northwest of the study area to the center. The spatial center of HEPEpsd moved from the northwest of the study area to the southwest of the study area. The expansion area of HEPEsm was nearly 1.5 times larger than that of HEPEpsd. The expansion areas of HEPEpsd aggregated in the old downtown, in which the contribution of HEPEpsd was greater than 90%. Thus, this study introduced various source data to build an easier and reliable method to map total exceedance PM2.5 exposure risk. Consequently, exposure risk results provided foundations to develop PM2.5 pollution mitigation strategies as well as scientific supports for sustainability and eco‐health achievement., Key Points Total exceedance PM2.5 exposure risk, including youths and older adults, was mappedThe hourly exceedance PM2.5 exposure risk (HEPE)psd was more aggregated than the HEPEsm Contribution of HEPEpsd varied geographically with percentage more than 50%

The concept of resilience is multi‐faceted. This commentary builds upon the analytical distinctions of resilience provided by Urquiza et al. (2021, https://doi.org/10.1029/2020EF001508). In response to this article, we emphasize several distinctions between resilience and other systems concepts. These include distinctions between resilience, risk, and vulnerability, the tradeoff between resilience and efficiency, resilience contrasted with robustness, the relationship between resilience and sustainability, and finally methods for building resilience‐by‐design or resilience‐by‐intervention. Improving understanding of these concepts will enable planners to select resilience strategies that best support their system goals. We use examples from the 2020–2021 coronavirus pandemic to illustrate the concepts and the juxtapositions between them., Key Points Resilience is one of many properties of systems affected by threat and resilience differ from robustness, sustainability, and riskBuilding resilience will require tradeoff on efficiency in complex systemsResilience can be build by design and by intervention

Global air pollution and climate change are major threats to planetary health. These threats are strongly linked through the short‐lived climate forcers (SLCFs); ozone (O3), aerosols, and methane (CH4). Understanding the impacts of ambitious SLCF mitigation in different source emission sectors on planetary health indicators can help prioritize international air pollution control strategies. A global Earth system model is applied to quantify the impacts of idealized 50% sustained reductions in year 2005 emissions in the eight largest global anthropogenic source sectors on the SLCFs and three indicators of planetary health: global mean surface air temperature change (∆GSAT), avoided PM2.5‐related premature mortalities and gross primary productivity (GPP). The model represents fully coupled atmospheric chemistry, aerosols, land ecosystems and climate, and includes dynamic CH4. Avoided global warming is modest, with largest impacts from 50% cuts in domestic (−0.085 K), agriculture (−0.034 K), and waste/landfill (−0.033 K). The 50% cuts in energy, domestic, and agriculture sector emissions offer the largest opportunities to mitigate global PM2.5‐related health risk at around 5%–7% each. Such small global impacts underline the challenges ahead in achieving the World Health Organization aspirational goal of a 2/3 reduction in the number of deaths from air pollution by 2030. Uncertainty due to natural climate variability in PM2.5 is an important underplayed dimension in global health risk assessment that can vastly exceed uncertainty due to the concentration‐response functions at the large regional scale. Globally, cuts to agriculture and domestic sector emissions are the most attractive targets to achieve climate and health co‐benefits through SLCF mitigation., Key Points Quantify impacts of deep short‐lived climate forcer (SLCF) emission mitigation by source sector on global temperature and human and ecosystem healthUncertainties due to year‐to‐year meteorological variability in PM2.5 is an important dimension in global human health risk assessmentAchieving planetary health benefits from SLCF mitigation requires ambitious mitigation pathways that tackle multiple source sectors

As of July 27, 2020, COVID‐19 has caused 640,000 deaths worldwide and has had a major impact on people's productivity and lives. Analyzing the spatial distribution characteristics of COVID‐19 cases and their relationships with meteorological and environmental factors might help enrich our knowledge of virus transmission and formulate reasonable epidemic prevention strategies. Taking the cumulative confirmed cases in Hubei province from January 23, 2020, to April 8, 2020, as an example, this study analyzed the spatial evolution characteristics of confirmed COVID‐19 cases in Hubei province using exploratory spatial data analysis and explored the spatial relationship between the main environmental and meteorological factors and confirmed COVID‐19 cases using a geographically weighted regression (GWR) model. Results show that there was no obvious spatial clustering of confirmed COVID‐19 cases in Hubei province, while the decline and end of the newly confirmed cases revealed relatively obvious negative spatial correlations. Due to the lockdown in Hubei province, the main air quality indexes (e.g., AQI and PM2.5) decreased significantly and environmental quality was better than historical contemporaneous levels. Meanwhile, the results of the GWR model suggest that the impacts of environmental and meteorological factors on the development of COVID‐19 were not significant. These findings indicate that measures such as social distancing and isolation played the primary role in controlling the development of the COVID‐19 epidemic., Key Points The spatiotemporal patterns of COVID‐19 cases in Hubei Province were analyzedAnomalies of environmental and meteorological factors contributed little to COVID‐19 developmentSocial distancing and isolation played a key role in controlling the epidemic

The ongoing coronavirus disease 2019 (COVID‐19) pandemic has caused more than 150 million cases of infection to date and poses a serious threat to global public health. In this study, global COVID‐19 data were used to examine the dynamical variations from the perspectives of immunity and contact of 84 countries across the five climate regions: tropical, arid, temperate, and cold. A new approach named Yi Hua Jie Mu is proposed to obtain the transmission rates based on the COVID‐19 data between the countries with the same climate region over the Northern Hemisphere and Southern Hemisphere. Our results suggest that the COVID‐19 pandemic will persist over a long period of time or enter into regular circulation in multiple periods of 1–2 years. Moreover, based on the simulated results by the COVID‐19 data, it is found that the temperate and cold climate regions have higher infection rates than the tropical and arid climate regions, which indicates that climate may modulate the transmission of COVID‐19. The role of the climate on the COVID‐19 variations should be concluded with more data and more cautions. The non‐pharmaceutical interventions still play the key role in controlling and prevention this global pandemic., Key Points A new approached is proposed to predict the future COVID‐19 variations rather than relying on information on other corona virusesCOVID‐19 pandemic will persist in multiple periods of 1–2 yearsThe temperate and cold climate regions have higher infection rates than the tropical and arid climate regions

The ongoing Coronavirus Disease 2019 (COVID‐19) has posed a serious threat to human public health and global economy. Population mobility is an important factor that drives the spread of COVID‐19. This study aimed to quantitatively evaluate the impact of population flow on the spread of COVID‐19 from a spatiotemporal perspective. To this end, a case study was carried out in Hubei Province, which was once the most affected area of COVID‐19 outbreak in Mainland China. The geographically and temporally weighted regression (GTWR) model was applied to model the spatiotemporal association between COVID‐19 epidemic and population mobility. Two patterns of population flows, including the population inflow from Wuhan and intra‐city population movement, were considered to construct explanatory variables. Results indicate that the GTWR model can reveal the spatial–temporal‐varying relationships between COVID‐19 and population mobility. Moreover, the association between COVID‐19 case counts and population movements presented three stages of temporal variation characteristics due to the virus incubation period and implementation of strict lockdown measures. In the spatial dimension, evident geographical disparities were observed across Hubei Province. These findings can provide policymakers useful knowledge about the impact of population movement on the spatio‐temporal transmission of COVID‐19. Thus, targeted interventions, if necessary in certain time periods, can be implemented to restrict population flow in cities with high transmission risk., Key Points To quantitatively evaluate how the effect of population flows on COVID‐19 spread varies over time and space in Hubei Province, ChinaThe association showed three stages of temporal variation characteristics and strong geographical disparities across Hubei ProvinceThe implementation of control measures by restricting population movements can play a key role in mitigating the spread of this epidemic

Due to the specific hydrothermal conditions of dry‐hot valleys, temperature changes caused by the development of large‐scale hydropower projects may be more extreme than they are in other regions. In this study, we analyzed these temperature changes at four hydropower stations in both dry‐hot and non‐dry‐hot valleys. Based on the calculated relative temperatures of the downstream river and the areas surrounding the reservoirs, we employed two indices to quantify the influence of the reservoirs on the temperatures of these two regions: the downstream river temperature change and the reservoir effect change intensity. Our results are as follows: (a) In the downstream rivers, the temperature regulation effect was more pronounced in the wet season; in the regions surrounding the reservoirs, the temperature regulation effect was more pronounced in the dry season. (b) The downstream river temperature in both the dry‐hot and wet‐hot valleys exhibited noticeable warming in both the wet and dry seasons, while the cold‐dry valley was characterized by cooling in the dry season and warming in the wet season. With the exception of the Liyuan station (where the influence of the reservoir on the downstream temperatures only extended to a distance of 9 km from the dam) during the dry season, the existence of the hydropower stations affected the temperatures of the entire downstream region. (c) For the areas surrounding the reservoir, the presence of a hydropower station mainly caused the temperatures in the dry‐hot valleys to rise and the temperatures in the non‐dry‐hot valleys to decrease., Key Points The difference of surface temperature affected by reservoirs was compared in dry‐hot and non‐dry‐hot valleysTo facilitate comparison of the different downstream river temperature changes, downstream river temperature change index was establishedThe reservoir effect change intensity was established to explore how reservoirs regulate the surface temperature in the surrounding areas

Although uncommon, nontuberculous mycobacterial (NTM) pulmonary infection in the Hawaiian Islands has a relatively high incidence and mortality compared to the mainland U.S. As a result, this study examines the possible geological and hydrological pathways by which NTM patients may become infected, including the environmental conditions that may favor growth and transport. Previously suggested infection routes include the inhalation of NTM attached to micro‐droplets from infected home plumbing systems and aerosolized dust from garden soil. In this study, we evaluate the possible routes NTM may take from riparian environments, into groundwater, into public water supplies and then into homes. Because NTM are notoriously hydrophobic and prone to attach to surfaces, mineralogy, and surface chemistry of suspended sediment in streams, soils, and rock scrapings suggest that NTM may especially attach to Fe‐oxides/hydroxides, and be transported as particles from losing streams to the aquifer on time‐scales of minutes to days. Within the aquifer, flow models indicate that water may be drawn into production wells on time scales (months) that permit NTM to survive and enter domestic water supplies. These processes depend on the presence of interconnected fracture networks with sufficient aperture to preclude complete autofiltration. The common occurrence of NTM in and around streams, in addition to wells, implies that the natural and built environments are capable of introducing a source of NTM into domestic water supplies via groundwater withdrawals. This may produce a persistent source of NTM infection to individuals through the presence of NTM‐laden biofilms in home plumbing., Key Points Nontuberculous mycobacterial (NTM) are found in soils and biofilms of riparian environments in Hawai'iNTM are likely transported from losing streams to aquifersPumped wells draw NTM into culinary water supplies and into homes. NTM are chlorine resistant and outcompete other taxa in home plumbing

The intensification of heat stress reduces the labor capacity and hence poses a threat to socio‐economic development. The reliable projection of the changing climate and the development of sound adaptation strategies are thus desired for adapting to the decreasing labor productivity under climate change. In this study, an optimization modeling approach coupled with dynamical downscaling is proposed to design the optimal adaptation strategies for improving labor productivity under heat stress in China. The future changes in heat stress represented by the wet‐bulb globe temperature (WBGT) are projected with a spatial resolution of 25 × 25 km by a regional climate model (RCM) through the dynamical downscaling of its driving global climate model (GCM). Uncertain information such as system costs, environmental costs, and subsidies are also incorporated into the optimization process to provide reliable decision alternatives for improving labor productivity. Results indicate that the intensification of WBGT is overestimated by the GCM compared to the RCM. Such an overestimation can lead to more losses in working hours derived from the GCM than those from the RCM regardless of climate scenarios. Nevertheless, the overestimated heat stress does not alter the regional measures taken to adapt to decreasing labor productivity. Compared to inland regions, the monsoon‐affected regions tend to improve labor productivity by applying air conditioning rather than working overtime due to the cost differences. Consequently, decision‐makers need to optimally make a balance between working overtime and air conditioning measures to meet sustainable development goals., Key Points An optimization model coupled with dynamical downscaling is developed to improve labor productivity under intensified heat stress in ChinaCivilian workers in inland regions will be more vulnerable to the intensified heat stress in a changing climateCompared to the regional climate model, the global climate model overestimates heat stress and thus exaggerates related system costs to recover from the working hour loss

Compound climate extremes, such as events with concurrent temperature and precipitation extremes, have significant impacts on the health of humans and ecosystems. This paper aims to analyze temporal and spatial characteristics of compound extremes of monthly temperature and precipitation, evaluate the performance of the sixth phase of the Coupled Model Intercomparison Project (CMIP6) models in simulating compound extremes, and investigate their future changes under Shared Socioeconomic Pathways (SSPs). The results show a significant increase in the frequency of compound warm extremes (warm/dry and warm/wet) but a decrease in compound cold extremes (cold/dry and cold/wet) during 1985–2014 relative to 1955–1984. The observed upward trends of compound warm extremes over China are much higher than those worldwide during the period of interest. A multi‐model ensemble (MME) of CMIP6 models performs well in simulating temporal changes of warm/wet extremes, and temporal correlation coefficients between MME and observations are above 0.86. Under future scenarios, CMIP6 simulations show substantial rises in compound warm extremes and declines in compound cold extremes. Globally, the average frequency of warm/wet extremes over a 30‐yr period is projected to increase for 2070–2099 relative to 1985–2014 by 18.53, 34.15, 48.79, and 59.60 under SSP1‐2.6, SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5, respectively. Inter‐model uncertainties for the frequencies of compound warm extremes are considerably higher than those of compound cold extremes. The projected uncertainties in the global occurrences of warm/wet extremes are 3.82 times those of warm/dry extremes during 2070–2099 and especially high for the Amazon and the Tibetan Plateau., Key Points A multi‐model ensemble of CMIP6 models performs well in simulating temporal changes of warm/wet extremesThe inter‐model uncertainties for the frequencies of compound warm extremes are considerably higher than those of compound cold extremesThe projected uncertainties in the global occurrences of warm/wet extremes are 3.82 times those of warm/dry extremes during 2070–2099