Performance of air temperature from ERA5-Land reanalysis in coastal urban agglomeration of Southeast China

The short-term effects of individual and mixed ambient air pollutants on suicide mortality: A case-crossover study

It is critical to explore intervenable environmental factors in suicide mortality. Based on 30,688 suicide cases obtained from the Mortality Surveillance System of the Jiangsu Provincial Centre for Disease Control and Prevention, we utilized a case-crossover design, and found that the OR of suicide deaths increased by a maximum of 0.71 % (95 % CI: 0.09 %, 1.32 %), 0.68 % (95 % CI: 0.12 %, 1.25 %), 0.77 % (95 % CI: 0.19 %, 1.37 %), 2.95 % (95 % CI: 1.62 %, 4.29 %), 4.18 % (95 % CI: 1.55 %, 6.88 %), and 0.93 % (95 % CI: 0.10 %, 1.77 %), respectively, for per 10 μg/m3 increase in the particulate matter (PM) with diameters ≤ 2.5 µm (PM2.5), PM with diameters ≤ 10 µm (PM10), ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), and per 0.1 mg/m3 increase in carbon monoxide (CO) concentrations with the conditional logistic regression analysis. People living in county-level cities were more susceptible. Particularly, a significant positive association was found between air pollutant mixture exposure and suicide deaths (OR=1.04,95 % CI: 1.01, 1.06). The excess fraction of suicide deaths due to air pollution reached a maximum of 8.07 %. In conclusion, we found associations between individual and mixed ambient air pollutants and suicide deaths, informing the development of integrated air pollution management and targeted measures for suicide prevention and intervention. ENVIRONMENTAL IMPLICATION: As a major contributor to the global burden of disease, air pollution was confirmed by accumulating studies to have adverse impact on mental health, and potentially lead to suicide deaths. However, systematic studies on the association between air pollution and suicide mortality are lacking. We explored the associations of multiple air pollutants and pollution mixtures with suicide deaths and assessed excess suicide mortality due to air pollution, emphasizing the importance of air pollution control on suicide prevention. Our study provides evidence to support mechanistic studies on the association between air pollution and suicide, and informs comprehensive air pollution management.

Daily station-level records of air temperature, snow depth, and ground temperature in the Northern Hemisphere

Air temperature (Ta), snow depth (Sd), and soil temperature (Tg) are crucial variables for studying the above- and below-ground thermal conditions, especially in high latitudes. However, in-situ observations are frequently sparse and inconsistent across various datasets, with a significant amount of missing data. This study has assembled a comprehensive dataset of in-situ observations of Ta, Sd, and Tg for the Northern Hemisphere (higher than 30°N latitude), spanning 1960-2021. This dataset encompasses metadata and daily data time series for 27,768, 32,417, and 659 gages for Ta, Sd, and Tg, respectively. Using the ERA5-Land reanalysis data product, we applied deep learning methodology to reconstruct the missing data that account for 54.5%, 59.3%, and 74.3% of Ta, Sd, and Tg daily time series, respectively. The obtained high temporal resolution dataset can be used to better understand physical phenomena and relevant mechanisms, such as the dynamics of land-surface-atmosphere energy exchange, snowpack, and permafrost.

Parsimonious estimation of hourly surface ozone concentration across China during 2015-2020

Surface ozone is an important air pollutant detrimental to human health and vegetation productivity, particularly in China. However, high resolution surface ozone concentration data is still lacking, largely hindering accurate assessment of associated environmental impacts. Here, we collected hourly ground ozone observations (over 6 million records), remote sensing products, meteorological data, and social-economic information, and applied recurrent neural networks to map hourly surface ozone data (HrSOD) at a 0.1° × 0.1° resolution across China during 2015-2020. The coefficient of determination (R2) values in sample-based, site-based, and by-year cross-validations were 0.72, 0.65 and 0.71, respectively, with the root mean square error (RMSE) values being 11.71 ppb (mean = 30.89 ppb), 12.81 ppb (mean = 30.96 ppb) and 11.14 ppb (mean = 31.26 ppb). Moreover, it exhibits high spatiotemporal consistency with ground-level observations at different time scales (diurnal, seasonal, annual), and at various spatial levels (individual sites and regional scales). Meanwhile, the HrSOD provides critical information for fine-resolution assessment of surface ozone impacts on environmental and human benefits.

The joint effects of mixture exposure to multiple meteorological factors on step count: A panel study in China

The public health burden of increasing extreme weather events has been well documented. However, the influence of meteorological factors on physical activity remains limited. Existing mixture effect methods cannot handle cumulative lag effects. Therefore, we developed quantile g-computation Distributed lag non-linear model (QG-DLNM) by embedding a DLNM into quantile g-computation to allow for the concurrent consideration of both cumulated lag effects and mixture effects. We gathered repeated measurement data from Henan Province in China to investigate both the individual impact of meteorological factor on step counts using a DLNM, and the joint effect using the QG-DLNM. We projected future step counts linked to changes in temperature and relative humidity driven by climate change under three scenarios from the sixth phase of the Coupled Model Intercomparison Project. Our findings indicate there are inversed U-shaped associations for temperature, wind speed, and mixture exposure with step counts, peaking at 11.6 °C in temperature, 2.7 m/s in wind speed, and 30th percentile in mixture exposure. However, there are negative associations between relative humidity and rainfall with step counts. Additionally, relative humidity possesses the highest weights in the joint effect (49% contribution). Compared to 2022s, future step counts are projected to decrease due to temperature changes, while increase due to relative humidity changes. However, when considering both future temperature and humidity changes driven by climate change, the projections indicate a decrease in step counts. Our findings may suggest Chinese physical activity will be negatively influenced by global warming.

Influence paradigms of soil moisture on land surface energy partitioning under different climatic conditions

Soil moisture (SM) directly controls the land surface energy partition which plays an important role in the formation of extreme weather events. However, its dependence on specific climatic conditions is not thoroughly understood due to the complexity of soil moisture effects. Here, we examine the relationship between SM and surface energy partitioning under different climate conditions, and identify the influence paradigms of soil moisture on surface energy partition. We find that temperature changes can explicitly determine the impact paradigm of different physical processes, i.e. evapotranspiration, soil freezing and thawing, and such influence paradigms are also affected by atmospheric aridity (VPD). Globally, there are five paradigms that effects on surface energy partitioning, including the warm-wet paradigm (WW), transitional paradigm (TP), warm-dry paradigm (WD), cool-wet paradigm (CW) and cold paradigm (CP). Since 1981, the global area proportion for TP is observed to increase pronouncedly. We also find that the critical SM threshold exhibits regional variations and the global average is 0.45 m3/m3. The identified paradigms and their long-term change trends provide new insights into the global intensification of land-atmosphere interaction, which has important implications for global warming and the formation of heatwaves.

Effects of temperature anomaly on sperm quality: A multi-center study of 33,234 men

Backgrounds

Global fertility rates continue to decline and sperm quality is a prime factor affecting male fertility. Both extreme cold and heat have been demonstrated to be associated with decreased sperm quality, but no epidemiological studies have considered human adaptation to long-term temperature. Our aim was to conduct a multi-center retrospective cohort study to investigate exposure-response relationship between temperature anomaly (TA) that deviate from long-term climate patterns and sperm quality.

Methods

A total of 78,952 semen samples measured in 33,234 donors from 6 provincial human sperm banks in China were collected. This study considered heat and cold acclimatization to prolonged exposure in humans and explored the exposure-response relationship between TAs and sperm quality parameters (sperm concentrations, sperm count, progressive motility, progressive sperm count, total motility and total motile sperm count) during the hot and cold seasons, respectively. Linear mixed models and generalized linear models were built separately for specific centers to pool in a meta-analysis to obtain the pooled effect of TA on sperm quality, considering repeated measurements data structure and spatial heterogeneity.

Results

We identified an inverted U-shaped exposure-response relationship between TA and sperm quality during the hot season. Significant negative effect of anomalous cold on sperm quality during the hot season was found after additional adjustment for Body mass index, marital status and childbearing history. The heat-related TA in hot season was significantly negatively associated with sperm concentration, progressive sperm count and total motile sperm count (all P-values<0.05). After adjusting the relative humidity, the cold-related TA in cold season was negatively associated with the sperm total motility (P-values<0.05).

Conclusions

Our results suggest both heat-related and cold-related TAs are associated with decreased sperm quality. The findings highlight the importance of reducing exposure to anomalous temperatures to protect male fertility.

Hourly values of an advanced human-biometeorological index for diverse populations from 1991 to 2020 in Greece

Existing assessments of the thermal-related impact of the environment on humans are often limited by the use of data that are not representative of the population exposure and/or not consider a human centred approach. Here, we combine high resolution regional retrospective analysis (reanalysis), population data and human energy balance modelling, in order to produce a human thermal bioclimate dataset capable of addressing the above limitations. The dataset consists of hourly, population-weighted values of an advanced human-biometeorological index, namely the modified physiologically equivalent temperature (mPET), at fine-scale administrative level and for 10 different population groups. It also includes the main environmental drivers of mPET at the same spatiotemporal resolution, covering the period from 1991 to 2020. The study area is Greece, but the provided code allows for the ease replication of the dataset in countries included in the domains of the climate reanalysis and population data, which focus over Europe. Thus, the presented data and code can be exploited for human-biometeorological and environmental epidemiological studies in the European continent.

Temperature-related death burden of various neurodegenerative diseases under climate warming: a nationwide modelling study

Limited knowledge exists regarding the ramifications of climate warming on death burden from neurodegenerative diseases. Here, we conducted a nationwide, individual-level, case-crossover study between 2013 and 2019 to investigate the effects of non-optimal temperatures on various neurodegenerative diseases and to predict the potential death burden under different climate change scenarios. Our findings reveal that both low and high temperatures are linked to increased risks of neurodegenerative diseases death. We project that heat-related neurodegenerative disease deaths would increase, while cold-related deaths would decrease. This is characterized by a steeper slope in the high-emission scenario, but a less pronounced trend in the scenarios involving mitigation strategies. Furthermore, we predict that the net changes in attributable death would increase after the mid-21st century, especially under the unrestricted-emission scenario. These results highlight the urgent need for effective climate and public health policies to address the growing challenges of neurodegenerative diseases associated with global warming.

Socio-economic factors and its influence on the association between temperature and dengue incidence in 61 Provinces of the Philippines, 2010-2019

BACKGROUND

Temperature has a significant impact on dengue incidence, however, changes on the temperature-dengue relationship across axes of socio-economic vulnerability is not well described. This study sought to determine the association between dengue and temperature in multiple locations in the Philippines and explore the effect modification by socio-economic factors.

METHOD

Nationwide dengue cases per province from 2010 to 2019 and data on temperature were obtained from the Philippines' Department of Health-Epidemiological Bureau and ERA5-land, respectively. A generalized additive mixed model (GAMM) with a distributed lag non-linear model was utilized to examine the association between temperature and dengue incidence. We further implemented an interaction analysis in determining how socio-economic factors modify the association. All analyses were implemented using R programming.

RESULTS

Nationwide temperature-dengue risk function was noted to depict an inverted U-shaped pattern. Dengue risk increased linearly alongside increasing mean temperature from 15.8 degrees Celsius and peaking at 27.5 degrees Celsius before declining. However, province-specific analyses revealed significant heterogeneity. Socio-economic factors had varying impact on the temperature-dengue association. Provinces with high population density, less people in urban areas with larger household size, high poverty incidence, higher health spending per capita, and in lower latitudes were noted to exhibit statistically higher dengue risk compared to their counterparts at the upper temperature range.

CONCLUSIONS

This observational study found that temperature was associated with dengue incidence, and that this association is more apparent in locations with high population density, less people in urban areas with larger household size, high poverty incidence, higher health spending per capita, and in lower latitudes. Differences with socio-economic conditions is linked with dengue risk. This highlights the need to develop interventions tailor-fit to local conditions.

Thermophysical Characteristics of Clay for Efficient Rammed Earth Wall Construction

This case study focuses on twelve compacted clay soil samples to understand their fundamental physical and thermal properties. For each sample, the density, thermal conductivity, thermal diffusivity, specific heat, and drying shrinkage were assessed. The identification and characterisation of the materials were also carried out by positioning them into the ternary diagram based on the percentage of sand, silt, and clay. These properties are definitive for the performance characteristics of materials used in rammed earth wall construction. The aim is to provide information for better knowledge and prediction regarding the dynamic heat flow in rammed earth walls. Experimental results show a relatively wide range of values for each property, reflecting the diverse properties of the sampled clays. The thermophysical characteristics of the 12 types of earth analysed showed correlations with reports in the literature in terms of density (1490-2150 kg/m3), porosity (23.22-39.99%), specific heat capacity (701-999 J/kgK), and thermal conductivity (0.523-1.209 W/mK), which indicates them as materials suitable for use in the construction of rammed earth walls. Using test data, a dynamic assessment of heat flow through simulated rammed earth walls was performed. For a better understanding of the results obtained, they were compared with results obtained for simulations where the building element would be made of concrete, i.e., a mineral wool core composite. Thus, heat flux at the wall surface and mass flux, respectively, during the 16 years of operation showed similar evolution for all 12 types of clay material analysed, with small variations explained by differences in thermophysical characteristics specific to each type of S1-S12 earth. In the case of walls made from clay material, there is a stabilisation in the evolution of the water content phenomenon by the 5th year of simulation. This contrasts with walls made of concrete, where the characteristic water content appears to evolve continuously over the 16-year period. Therefore, it can be said that in the case of the construction elements of existing buildings, which have already gone through a sufficient period for the maturation of the materials in their construction elements, the rammed earth wall quickly develops a moisture buffer function. In the case of simulating a mineral wool core composite wall, it cannot perform as a temperature or humidity buffer, exhibiting an enthalpy exchange with indoor air that is only 4% of that of the rammed earth walls; consequently, it does not play a significant role in regulating indoor comfort conditions. Overall, there is confirmation of the temperature and moisture buffering capabilities of rammed earth walls during both warm and cold periods of the year, which is consistent with other reports in the literature. The findings of this research provide a better insight into clay as a material for rammed earth walls for more efficient design and construction, offering potential improvements regarding indoor comfort, energy efficiency, and sustainability. The data also provides useful information in the fields of architecture and civil engineering regarding the use of clay as an eco-friendly building material. The results emphasise the importance of thoroughly understanding the thermophysical properties of clay to ensure the efficiency of rammed earth construction.

Evidences of localized coastal warming near major urban centres along the Indian coastline: past and future trends

Large-scale urbanization near the coasts is reported to directly impact physical and biogeochemical characteristics of near shore waters, through hydro-meteorological forcing, developing abnormalities such as coastal warming. This study attempts to understand the impact-magnitude of urban expansion on coastal sea surface temperature (SST) rise in the vicinity of six major cities along the Indian coastline. Different parameters such as air temperature (AT), relative humidity (RH), wind speed (WS), precipitation (P), land surface temperature (LST) and aerosol optical depth (AOD) representing the climate over the cities were analysed and AT was found to have highest correlation with increasing coastal SST values, specifically, along the western coast (R² > 0.93). Autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) models were employed to analyse past (1980-2019) and forecast future (2020-2029) SST trends off all urban coasts. ANN provided comparatively better prediction accuracy with RMSE values ranging from 0.40 to 0.76 K compared to the seasonal ARIMA model (RMSE: 0.60-1 K). Prediction accuracy further improved by coupling ANN with discrete wavelet transformation (DWT) which could reduce the data noise (RMSE: 0.37-0.63 K). The entire study period (1980-2029) revealed significant and consistent increase in SST values (0.5-1 K) along the western coastal cities which varied considerably along the east coast (from north to south), indicating the influence of tropical cyclones combined with increased river influx. Such unnatural interferences in the dynamic land-atmosphere-ocean circulation not only render the coastal ecosystems vulnerable to degradation but also potentially develop a feedback effect which impacts the general climatology of the region.

Evaluation of ERA5-Land reanalysis datasets for extreme temperatures in the Qilian Mountains of China

An increase in extreme temperature events could have a significant impact on terrestrial ecosystems. Reanalysis temperature data are an important data set for extreme temperature estimation in mountainous areas with few meteorological stations. The ability of ERA5-Land reanalysis data to capture the extreme temperature index published by the Expert Team on Climate Change Detection and Indices (ETCCDI) was evaluated by using the observational data from 17 meteorological stations in the Qilian Mountains (QLM) during 1979–2017. The results show that the ERA5-Land reanalysis temperature data can capture well for the daily maximum temperature, two warm extremes (TXx and TX90p) and one cold extreme (FD0) in the QLM. ERA5-Land’s ability to capture temperature extremes is best in summer and worst in spring and winter. In addition, ERA5-Land can capture trends in all extreme temperature indices except the daily temperature range (DTR). The main bias of ERA5-Land is due to the difference in elevation between the ground observation station and the ERA5-Land grid point. The simulation accuracy of ERA5-Land increases with the decrease of elevation difference. The results can provide a reference for the study of local extreme temperature by using reanalysis data.

Accuracy assessment of temperature trends from ERA5 and ERA5-Land

Many environmental processes and ecological systems are being affected by the warming temperatures as a result of climate change. To correctly identify and attribute the uncertainty estimates in these systems, an investigation of temperature trend signal existing in the datasets that are used to study such systems is necessary. In this study, the trend of widely used ERA5 and ERA5-Land temperature estimates between 1951 and 2020 were validated using temperature trends from ground station-based observations in Turkey. The investigation included datasets obtained over 540 stations, and the seasonality and spatio-temporal variability of the trend accuracy was also investigated. On average, the trends of observations over all stations were 0.82 °C/decade and 0.30 °C/decade for the periods 2001-2020 and 1951-2020, respectively, indicating strong evidence of climate change. When the model datasets used spatially and temporally continuous datasets, the trends identified were 0.91 °C/decade and 0.21 °C/decade over the entire Turkey for the years 2001-2020 and 1951-2020, respectively. The difference in the 70-year trends of the two estimates was attributed to the missing datasets in observations. The differences between the trends of model estimates and observations were higher for the first decade than for the last two decades, stressing the impact of improved model estimates over time. All products showed heavy seasonality, suggesting that winter trends (1.3 °C/decade on average) are much higher than the summer (0.3 °C/decade) between 2001 and 2020. The results indicated a high degree of consistency between the trends of ERA5/ERA5-Land and observations, implying they may be used as a replacement to observations.

Spatio-Temporal Dynamic of the Land Use/Cover Change and Scenario Simulation in the Southeast Coastal Shelterbelt System Construction Project Region of China

The National Coastal Shelterbelt System Construction Project (NCSSCP) was proposed to increase the afforestation area and neutralize the impact of urbanization, especially in the southeast coastal sub-region of China. In this study, we identified the spatio-temporal evolution characteristics and predicted the land use and land cover changes (LUCC) associated with this project by modeling scenarios, seeking to explore the path of sustainable development. The spatial structure was analyzed using the landscape pattern index approach and the land use transfer matrix. By coupling the Markov model and patch-generating a land-use simulation model (PLUS), different scenarios were analyzed to predict the quantity and spatial changes. According to the results, based on the current trends and due to the impact of urbanization, the forest area was predicted to decrease by 633.19 km2, whilst appearing more spatially fragmented and separated. However, with the completion of the NCSSCP target, the forest area was predicted to increase by 1666.12 km2, and the spatial structure would appear more cohesive and concentrated. From an overall perspective, the afforestation target of NCSSCP will not be completed under the present trend. It is difficult for the afforestation speed of the NCSSCP to keep up with the speed of urbanization. Therefore, giving consideration to both the afforestation speed and quality and reducing the speed of urbanization to balance the economy and ecology would be beneficial in terms of the realization of the aims of sustainable development.