Daily Air Temperature Research Articles

High-resolution grids of daily air temperature for Peru - the new PISCOt v1.2 dataset

Gridded high-resolution climate datasets are increasingly important for a wide range of modelling applications. Here we present PISCOt (v1.2), a novel high spatial resolution (0.01°) dataset of daily air temperature for entire Peru (1981–2020). The dataset development involves four main steps: (i) quality control; (ii) gap-filling; (iii) homogenisation of weather stations, and (iv) spatial interpolation using additional data, a revised calculation sequence and an enhanced version control. This improved methodological framework enables capturing complex spatial variability of maximum and minimum air temperature at a more accurate scale compared to other existing datasets (e.g. PISCOt v1.1, ERA5-Land, TerraClimate, CHIRTS). PISCOt performs well with mean absolute errors of 1.4 °C and 1.2 °C for maximum and minimum air temperature, respectively. For the first time, PISCOt v1.2 adequately captures complex climatology at high spatiotemporal resolution and therefore provides a substantial improvement for numerous applications at local-regional level. This is particularly useful in view of data scarcity and urgently needed model-based decision making for climate change, water balance and ecosystem assessment studies in Peru.

Modern changes in climatic characteristics and minimum flow of the rivers in the basin of lake Sevan

Formulation of the problem. Studies and data on modern changes in the winter minimum average monthly flow of the rivers of the Lake Sevan basin and their climatic characteristics have acquired great practical importance and considerable interest due to the increased use of water resources for the purposes of various types of economic activities, in particular, in connection with the development of the economy, hydropower, irrigation, population growth and rising living standards. Especially relevant are the calculations of the minimum flow in relation to solving the problems of water supply for settlements, industrial enterprises. The aim of the work. Assessment of modern changes in the winter minimum average monthly runoff of rivers in the Lake Sevan basin and their climatic characteristics. Methods. The following methods were used in the work: mathematical-statistical, extrapolation, interpolation, spatial analysis, analogy, correlation and cartography. Results. A physical map has been compiled with a network of meteorological stations and hydrological posts in the Lake Sevan basin. Close correlations were obtained between the values of the winter minimum average monthly runoff and the average runoff of the winter period for a hydrological year. These dependences can be used for preliminary estimates of the minimum runoff of the winter low-water period of unstudied rivers in the territory under consideration. In the course of the research, the winter minimum average monthly discharges were calculated by months and for the entire observation period, the average value of the minimum runoff for the winter low water, and the linear trend of the climatic characteristic. Based on the obtained results, a map of the distribution of the modul of the average minimum runoff of the winter low-water period and the coefficients of the linear trend of the winter air temperature were constructed. Examples of curves of long-term changes in precipitation amounts for the autumn and winter periods, the sum of positive temperatures and the number of days with a positive average daily air temperature in the winter period for a series of meteorological stations in the study area are presented. The rivers of the Lake Sevan basin are characterized by an uneven spatio-temporal distribution of winter minimum runoff and climatic characteristics, changes in the winter average monthly minimum runoff, precipitation amounts for the autumn and winter periods are multidirectional, and air temperature changes are only an upward trend. In the area under consideration, there is mainly (by 8 out of 12 stations under study) a tendency to decrease in the values of the minimum average monthly river flow. The conducted studies lead us to the conclusion that the main factor causing changes in the low-water runoff of the rivers in the Lake Sevan basin in winter is the air temperature, or rather, its increase. It follows from this that when calculating and forecasting winter runoff, it is necessary to relate the values of runoff and air temperature.

Sub-Shrub Components Change the Soil Water Storage Response to Daily Precipitation and Air Temperature in the Loess Plateau

Soil water shortage has become a severe issue in ecological restoration and sustainable development in the Loess Plateau, facing the challenges of climate change and vegetation restoration. This study monitored the soil water content in surface soil (0–40 cm) with different sub-shrub component treatments, including the natural condition (NC), the canopy plus the roots (CR) and only the roots (OR), to analyze the change in soil water storage (∆W) and its response to precipitation (P) and air temperature (Ta) on a daily scale. P was the main factor controlling the daily ∆W, contributing 49–52% to the variation in the daily ∆W, and Ta only explained 6–21% of the variation. Minimum P amounts of 0.74–1.12 mm and maximum Ta of 29.09–32.00 °C were the thresholds required to increase soil water storage (W). Sub-shrub components showed significant influences on soil water conservation. We found that the ∆W hierarchy for each sub-shrub treatment was NC (1.73 mm) > CR (0.71 mm) > OR (0.56 mm) on rainy days and NC (−0.53 mm) < CR (−0.36 mm) < OR (−0.06 mm) on no-rain days. Additionally, the hierarchy of the rainwater retention rate was NC (26.43%) > OR (13.71%) > CR (4.58%). Thus, a canopy could increase infiltration and hugely consume soil water at the same time, while litter could weaken or offset the canopy’s effects and the roots promote infiltration with little evaporation loss.

Microclimate Characteristics in Mangrove Forest Areas in Padang City

Mangrove forest microclimate is the climatic conditions that occur in mangrove forests. The research aims to determine the conditions and characteristics of the microclimate (air temperature, air humidity and light intensity) in the mangrove forest area. The research location was chosen in the Taluak Buo mangrove forest area because the conditions and characteristics of the microclimate are not yet known. The data used is prime data obtained from direct measurements in the field. Measurements of several weather elements were carried out three times, namely in the morning (07.00-08.00 WIB), in the noon (12.00-13.00 WIB), and in the afternoon (17.00-18.00 WIB) for 7 days at three observation points using an anemometer instrument. The results obtained from this research in the Taluak Buo mangrove forest area in the Bungus Teluk Kabung sub-district are an average daily air temperature of 26.94°C in the forest, 27.94°C at the position on the edge of the forest and 28.6°C at outside the forest. The average difference in air temperature between inside the forest and outside the forest is between 1,22-2,14°C. On the other hand, the difference in air humidity is 5-6%. The average difference in sunlight intensity between inside the forest and outside the forest is between 3.923,44-22,462,66 lux. The air temperature and intensity of sunlight inside the forest is lower than at the edge and outside the forest, which is inversely proportional to the higher air humidity inside the forest.

Generalized Partially Functional Linear Model with Unknown Link Function

In existing models with an unknown link function, the issue of predictors containing both multiple functional data and multiple scalar data has not been studied. To fill this gap, we propose a generalized partially functional linear model, which not only models the relationship between multiple scalar and functional predictors and responses, but also automatically estimates the link function. Specifically, we use the functional principal component analysis method to reduce the dimensionality of functional predictors, estimate the regression coefficients using the maximum likelihood estimation method, estimate the link function using the method of local linear regression, iteratively obtain the final estimator, and establish the asymptotic normality of the estimator. The asymptotic normality is illustrated through simulation experiments. Finally, the proposed model is applied to study the influence of environmental, economic, and medical levels on life expectancy in China. In the study, functional predictors are the daily air quality index, temperature, and humidity of 58 cities in 2020, and scalar predictors are GDP and the number of beds in hospitals. The experimental results indicate that the unknown link function model has a smaller prediction error and better performance than both the model with the known link function and the model without a link function.

Antarctic daily mesoscale air temperature dataset derived from MODIS land and ice surface temperature

Knowledge about local air temperature variations and extremes in Antarctica is of large interest to many polar disciplines such as climatology, glaciology, hydrology, and ecology and it is a key variable to understand climate change. Due to the remote and harsh conditions of Antarctica’s environment, the distribution of air temperature observations from Automatic Weather Stations is notably sparse across the region. Previous studies have shown that satellite-derived land and ice surface temperatures can be used as a suitable proxy for air temperature. Here, we developed a daily near-surface air temperature dataset, AntAir ICE for terrestrial Antarctica and the surrounding ice shelves by modelling air temperature from MODIS skin temperature for the period 2003 to 2021 using a linear model. AntAir ICE has a daily temporal resolution and a gridded spatial resolution of 1 km2. AntAir ICE has a higher accuracy in reproducing in-situ measured air temperature when compared with the well-established climate re-analysis model ERA5 and a higher spatial resolution which highlights its potential for monitoring temperature patterns in Antarctica.

Dynamics and variability of microclimate in an unmanaged mountain forest after a bark beetle outbreak

Forest disturbances alter hydrological, energy, and microclimatic characteristics and their extents vary with the landscape characteristics of the terrain. We evaluated changes in evapotranspiration and latent heat rate in an unmanaged, central European mountain forest (Norway spruce) after a bark beetle-induced mortality of >75 % trees, and the spatial variability in microclimate during its natural regeneration. We observed that (1) the annual mean incident solar radiation to the treeless plots was 8.2 MJ m−2 day−1 (95 W m−2), being reduced by ∼45 % compared to its theoretical flux by high average cloudiness (70.6%) and short daily sunshine (4.3 hour day−1). The observed energy input to the forest floor beneath survived canopies (1.2 MJ m−2 day−1) was an order of magnitude lower than solar radiation to the canopy surface. (2) The mean annual evapotranspiration and the average annual latent heat rate decreased by 155 mm yr−1 and 385 MJ m−2 yr−1 (12 W m−2), respectively, after tree dieback. (3) The soil and air temperatures at individual plots correlated positively with the incident solar radiation and negatively with elevation. Mean soil and air temperatures exhibited similar lapse rates of 5.9–8.0 °C km−1 during the growing season (May-October). The energy input (positive relationship) and elevation (negative relationship) explained 74–83 % of the between-plot variability in mean soil and air temperatures and 51–77 % of their daily amplitudes during the growing season. (4) On sunny days, the observed soil and air temperatures were significantly lower (by ∼50 and 15 %, respectively) than ground surface temperature based on thermal satellite data (Landsat-8). The ground surface temperature explained 80–92 % of the observed variability in the soil and air temperatures. (5) The between-plot differences in daily soil and air temperatures and their amplitudes were from 24 to 70 % explained by solar radiation (positive correlation) and relative air humidity and wind speed (negative correlations).

Spatiotemporal prediction of daily air temperature using remote sensing and machine learning in Morocco

Spatiotemporal prediction of daily air temperature using remote sensing and machine learning in Morocco

Peatland-VU-NUCOM (PVN 1.0): using dynamic plant functional types to model peatland vegetation, CH4, and CO2 emissions

Abstract. Despite covering only 3 % of the planet’s land surface, peatlands store 30 % of the planet’s terrestrial carbon. The net greenhouse gas (GHG) emissions from peatlands depend on many factors but primarily soil temperature, vegetation composition, water level and drainage, and land management. However, many peatland models rely on water levels to estimate CH4 exchange, neglecting to consider the role of CH4 transported to the atmosphere by vegetation. To assess the impact of vegetation on the GHG fluxes of peatlands, we have developed a new model, Peatland-VU-NUCOM (PVN). The PVN model is a site-specific peatland CH4 and CO2 emissions model, able to reproduce vegetation dynamics. To represent dynamic vegetation, we have introduced plant functional types and competition, adapted from the NUCOM-BOG model, into the framework of the Peatland-VU model, a peatland GHG emissions model. The new PVN model includes plant competition, CH4 diffusion, ebullition, root, shoot, litter, exudate production, belowground decomposition, and aboveground moss development under changing water levels and climatic conditions. Here, we present the PVN model structure and explore the model's sensitivity to environmental input data and the introduction of the new vegetation competition schemes. We evaluate the model against observed chamber data collected at two peatland sites in the Netherlands to show that the model is able to reproduce realistic plant biomass fractions and daily CH4 and CO2 fluxes. We find that daily air temperature, water level, harvest frequency and height, and vegetation composition drive CH4 and CO2 emissions. We find that this process-based model is suitable to be used to simulate peatland vegetation dynamics and CH4 and CO2 emissions.

Climate change impacts on streamflow, sediment load and carbon fluxes in the Lena River delta

Water and sediment supply are essential to the health of deltaic ecosystems. Diverse datasets were integrated to better understand how climate change is shifting the supply of water and sediment to the largest polar distributary channel pattern – the Lena River Delta. Here the increase in warming rate from an average air temperature is from 4.1 °C for the period 1950–99 to 6.1 °C during 2000–21, which is higher than in the adjacent polar regions. Streamflow and sediment yield entering the Lena Delta have increased since 1988 by 56.3 km3 and 6.1×106 t, respectively; meanwhile, the Lena River’s increases in water temperature in June, July–August and September were found to be as much as 1.1, 0.6 and 0.05 °C. These changes have a pronounced effect on sediment regimes in particular parts of the delta. Based on analyses of correlations between various hydroclimatic drivers and sediment concentration changes across particular distributaries of the Lena Delta extracted from Landsat datasets, bank degradation driven by thermal erosional processes (which are in turn related to air and soil temperature increases) is proved to be the primary factor of the sediment regime in the delta. The study also highlights that sediment load changes are sensitive to wind speed due to remobilization of bottom sediment. Sums of daily air temperature and wind speed over 3 days are correlated with sediment concentration changes in the delta. The results also indicate that carbon transport across the delta (both POC and DOC) depends on sediment transport conditions and water discharge and might increase by up to 10 %. We conclude that the Lena Delta can be recognized as the global hot spot in terms of the hydrological consequences of climate change, which is altering sediment regimes, stream hydromorphology and carbon transport.

Mitigating Heat Exposure: Exploring the Role of Knowledge, Risk Perception, and Precautionary Behavior

Increasing ambient temperatures due to climate change have significantly impacted workers’ physical and mental workloads, affecting their health and safety. Promoting precautionary behaviors, especially among outdoor workers, is an effective strategy widely recommended for addressing these challenges. Precautionary behavior represents an essential protective mechanism that can be adopted at the individual level. This study explores the connections among Occupational Safety and Health (OSH), risk perception, and precautionary behavior. Descriptive statistics and Structural Equation Modeling with the Partial Least Squares approach were used to analyze the data. This study was conducted at two forest management units in Central Java and East Java, Indonesia, where daily air temperatures are relatively high. The research involved 100 respondents, comprising 50 indoor and 50 outdoor workers. The findings of this investigation reveal that OSH-related knowledge among indoor workers predicts their precautionary behavior. Nevertheless, according to the model, attempts to improve precautionary behavior through knowledge enhancement are estimated to be unsuccessful among outdoor workers. Nonetheless, OSH-related knowledge can facilitate workers in developing a more realistic perception, especially regarding the “dread risk factor” among forestry workers, which can positively impact their precautionary behavior. Keywords: forestry worker, heat exposure, mediation analysis, occupational health, psychometrics paradigm

Physiological Response of Tomato and Cucumber Plants to Micro-Spray in High-Temperature Environment: A Scientific and Effective Means of Alleviating Crop Heat Stress

Drought and heat stresses are severe threats to crop production and food security in arid and semi-arid regions of the globe, especially during the summer. This study investigates the effects of micro-spray on micro-climatic and physiological characteristics of cucumber and tomato plants for four growing seasons (two growing seasons each (cucumber: 2017 and 2018 and tomato: 2021 and 2022)). The experiment was conducted in a Venlo-type greenhouse where plants were irrigated with a combined micro-spray and drip irrigation (MSDI) system (1 min for tomato (seven times a day) and 2 mins (four times a day) for cucumber) and drip irrigation (DI). Both plants’ growth and physiological parameters were recorded at the end of the experiment. Moreover, the experimental results showed that plant height and stem diameter increased under MSDI in all seasons. In addition, a micro-spray duration of 2 min decreased the average daily air temperature (Ta) and leaf temperature (T1) by 0.8 °C and 4.9 °C, respectively, and increased the average daily relative humidity (RH) by 4.3%. However, a micro-spray duration of 1 min reduced the average daily Ta and T1 by 0.76 °C and 4.6 °C, respectively, but the increase in RH (2.7%) was much lower than that in the single micro-spray duration of 2 min. In addition, the net leaf photosynthetic rate (Pn), the effective quantum yield of PSII (ΦPSII), and the stomatal conductance (Gs) of both plants increased under MSDI compared with DI. A K-means analysis showed that MSDI could eliminate the adverse effects of sunlight stress on cucumbers and tomatoes. MSDI increased the yield of cucumber and tomato plants by 18.9% and 40.4%, respectively. The fruit weight of cucumber (2017) and tomato (2021) under MSDI did not increase significantly compared to DI, which indicates that MSDI mitigated heat stress, prevented flowers from being burned, and increased the number of fruits. The results also suggested that the total soluble solids in the cucumbers and tomatoes showed no significant differences (p > 0.05) between the two treatments in four seasons. In conclusion, the MSDI system can be an appropriate strategy for the irrigation needs and climate control of plants grown in greenhouses during the hot season.

Stream groundwater inputs generate fine‐scale variation in brook trout phenology and growth across a warming landscape

Abstract Climate change is increasing global atmospheric temperatures, which can reduce abundance and cause range shifts in species that are sensitive to warming. However, fine‐scale thermal heterogeneity can drive highly variable local responses to climate change, especially in freshwater environments that differ in groundwater inputs and geomorphology. We used temperature data collected during 2012–2021 from 10 small, pristine streams in eastern Canada to characterise thermal variation at a small spatial scale (~25 km2). We then used relationships between daily air and stream temperatures to reconstruct stream temperature since 1980, and assessed how thermal variation influenced the phenology and growth of brook trout (Salvelinus fontinalis). Air–stream temperature relationships varied considerably among streams despite their close proximity, with predicted summer temperatures differing up to 9.5°C between warmer rainfall‐dominated streams and cooler groundwater‐dominated streams. Rainfall‐dominated streams warmed more than twice as fast as groundwater‐dominated streams across all seasons since 1980, with nearly four‐fold differences in rates of warming evident during summer months. Fine‐scale thermal heterogeneity also shaped brook trout phenology, as juveniles in rainfall‐dominated streams were estimated to hatch and emerge much later (~70 and 40 days, respectively) and experience faster phenological shifts than in groundwater‐dominated streams. Relationships between juvenile brook trout size and accumulated degree‐days were positive, but slopes differed over two‐fold and did not vary systematically based on stream hydrology, suggesting more idiosyncratic impacts of warming on early growth. Collectively, our study illustrates how species responses to climate change in freshwater environments can be consistent in direction but vary substantially in magnitude owing to the influence of groundwater. Future climate change is likely to increase the thermal stress experienced by brook trout populations in warmer rainfall‐dominated streams, while potentially benefiting those in groundwater‐dominated streams where current temperatures are often suboptimal. Observed differences in the rates and ecological impacts of warming among streams suggest that fine‐scale thermal variation must be considered when forecasting effects of future climate change on stream fish population dynamics.

Ogólna charakterystyka termiczna doliny Wisły w obszarze Warszawy = A general thermal characterisation of the Vistula Valley in Warsaw

Rivers flowing through cities and their valleys serve many functions, having also a special role in the amelioration of the urban climate. Within the city limits of Poland’s capital city, Warsaw, the River Vistula flows over a distance of 31.5 km, in a valley of maximum width 2 km. The river is regulated and enclosed within flood embankments. Its left bank, in the districts of Śródmieście and Żoliborz especially, is high and partly built over with boulevards; while the right bank is low and natural, with sandy beaches and walking and cycling paths. Despite the long-term transformation, the Vistula Valley still has an extremely valuable role in the natural system. Most of it enjoys Natura 2000 protection (from the points of view of flora, fauna and the Valley itself). While the Vistula Valley in Warsaw certainly constitutes the city’s main and most effective area of ventilation and air regeneration, knowledge of its thermal conditions remains scant. That is somewhat paradoxical, given worldwide study of urban climate since the 1830s, with considerable urban-focused interest among Polish climatologists also not leading to much elucidation of the river-valley climate within the city. Through the work carried out it proved possible to demonstrate differences in the thermal regime in Warsaw’s Vistula Valley, depending on the location and the immediate vicinity of the studied point. The amplitude in average daily air temperature is lower where density of buildings is greater, and where the ratio for the presence of biologically active areas is lower. The northern and southern parts of the Valley in Warsaw are characterised by similar thermal conditions, while the downtown part differs clearly from these places, being much warmer, but also much more likely to play host to the “cold spot” phenomenon.

Forecasting Daily Maximum and Minimum Air Temperatures in The Cilacap District Using Arima and Exponential Smoothing

This research aims to predict daily maximum and minimum air temperatures in Cilacap Regency using ARIMA and Exponential Smoothing. Data was obtained from recordings carried out by BMKG Cilacap using maximum and minimum thermometers taken from January 1, 2016, to December 31, 2021. The results show that the best forecasting model uses the ARIMA (2,1,2) model for maximum temperature and the ARIMA (1,1,1) model for minimum temperature, with the MAPE value of 2.09% for the maximum temperature and 2.44% for the minimum temperature, while the RMSE value obtained is 0.9177 for the maximum temperature and 0.8001 for the minimum temperature. Based on the ARIMA model, Cilacap's daily maximum temperature in 2022 was predicted to be around 30.6ᵒC, with a 95% confidence interval between 28ᵒC - 35ᵒC, while the minimum temperature was predicted to be around 25.1ᵒC, with a 95% confidence interval between 23ᵒC - 28ᵒC.

Dynamics of carbon budget and meteorological factors of a typical maize ecosystem in Songnen Plain, China

Aim of study: Understanding the carbon budget and meteorological factor impacts of farmland ecosystems is helpful for scientific assessment of carbon budget and low-carbon agricultural production practices. Area of study: The Songnen Plain, NE China, in 2019. Material and methods: Based on eddy-related flux and soil heterotrophic respiration observations from a typical maize farmland ecosystem, using mathematical statistics and carbon balance equation methods, were analyzed. Main results: Soil respiration rate (Rs) and composition were influenced and controlled by the synergistic effect of surface soil temperature (Ts) and water content (Wcs). Ts played a leading role, while Wcs played an important role. Ts and Wcs had the greatest influence on the heterotrophic respiration rate (Rh), followed by Rs and autotrophic respiration rate (Ra). Daily variations of net ecosystem productivity were correlated with daily mean air temperature, latent heat flux, and sensible heat flux. Annual carbon revenue was 1139.67 g C m-2, annual carbon expenditure was 456.14 g C m-2, and annual carbon budget was -683.53 g C m-2 in 2019. While considering that maize grain yield (-353.44 g C m-2) was moved out of the field at harvest, the net ecosystem carbon balance was -330.09 g C m-2; then it was carbon sink in 2019. By fully utilizing climate resources and improving agricultural managements, carbon sink is increased in farmland ecosystems. Research highlights: Soil respiration rate and composition were influenced and controlled by the synergistic effect of soil temperature and water content; the maize farmland ecosystem is carbon sink.

Teplá období duben až říjen v Česku 1961 až 2022

The article compares selected periods in the warm half year based on the areal average of daily air temperature maxima and the number of summer and hot days from 1961 to 2022. Several characteristics have been proposed to evaluate the exceptionality of these periods. The highest M-day averages, averages for selected periods, and the number of summer and hot days in those periods were compared. For comparison, an assessment was also included on the total rainfall and total runoff for the territory of the Czech Republic.

Climate risks and vulnerabilities of the Arabica coffee in Brazil under current and future climates considering new CMIP6 models

The susceptibility to climate change concerns the coffee market worldwide due to possible severe productivity losses. Brazil is the world's largest Arabica coffee producer and has crops in regions considered persistent climate change hotspots. Our study analyzed risks, vulnerabilities, and susceptibilities to pests and diseases in these regions under current and future climates and outlined adaptive measures to reduce future vulnerabilities. Ten risk indicators based on Arabica coffee requirements were proposed: water supply (Iw), base (TIB) and maximum temperature stresses (TImax), which delimit the temperature range where Arabica coffee grows and productivity is penalized outside both ranges, frost stress (TIfrost), diseases such as rust (DIrust), brown eye spot (DIbrown), and Phoma leaf spot (DIphoma), pests such as coffee berry borer (PIberry), coffee leaf miner (PIminer), and yield loss due to water stress (Iyg). Daily near-surface air temperature (minimum, mean, and maximum), relative humidity, precipitation, and global solar radiation were used from 16 General Circulation Models (GCMs) from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP), which are derived from the Coupled Model Intercomparison Project Phase 6 (CMIP6) in three Shared Socioeconomic Pathways scenarios (SSP245, SSP370 and SSP585). All risk indicators were calculated for the current climate (1995–2014) and projected for the near (2041–2060), intermediate (2061–2080), and far future (2081–2100) in three SSPs and then classified into five risk classes (very low, low, moderate, high and very high). Our results indicated that due to increases in TImax and Iyg indicators, with high to very high risk in area and magnitude, Arabica coffee plantations will be negatively affected and economically unfeasible for about 35 % to 75 % of the studied area throughout the 21st century. Furthermore, the rust and the leaf miner will remain a concern in future climates due to increased temperatures and reduced relative humidity. The future of Arabica coffee crops in Brazil will depend on adopting effective adaptive measures and prudent agricultural strategies to address anticipated risks, including shifting crops to higher altitude areas, introducing more climate-resilient coffee cultivars/varieties, using agroforestry or intercropping systems, planting in closer spacing or higher density planting, and employing dripper or partial root-zone irrigation techniques.

Short-Term Ambient Air Pollution Exposure and Risk of Out-of-Hospital Cardiac Arrest in Sweden: A Nationwide Case-Crossover Study.

Background Air pollution is one of the main risk factors for cardiovascular disease globally, but its association with out-of-hospital cardiac arrest at low air pollution levels is unclear. This nationwide study in Sweden aims to investigate if air pollution is associated with a higher risk of out-of-hospital cardiac arrest in an area with relatively low air pollution levels. Methods and Results This study was a nationwide time-stratified case-crossover study investigating the association between short-term air pollution exposures and out-of-hospital cardiac arrest using data from the SRCR (Swedish Registry for Cardiopulmonary Resuscitation) between 2009 and 2019. Daily air pollution levels were estimated in 1×1-km grids for all of Sweden using a satellite-based machine learning model. The association between daily air pollutant levels and out-of-hospital cardiac arrest was quantified using conditional logistic regression adjusted for daily air temperature. Particulate matter <2.5 μm exposure was associated with a higher risk of out-of-hospital cardiac arrest among a total of 29 604 cases. In a multipollutant model, the association was most pronounced for intermediate daily lags, with an increased relative risk of 6.2% (95% CI, 1.0-11.8) per 10 μg/m3 increase of particulate matter <2.5 μm 4 days before the event. A similar pattern of association was observed for particulate matter <10 μm. No clear association was observed for O3 and NO2. Conclusions Short-term exposure to air pollution was associated with higher risk of out-of-hospital cardiac arrest. The findings add to the evidence of an adverse effect of particulate matter on out-of-hospital cardiac arrest, even at very low levels below current regulatory standards.

Joint effect of heat and air pollution on mortality in 620 cities of 36 countries

BackgroundThe epidemiological evidence on the interaction between heat and ambient air pollution on mortality is still inconsistent. ObjectivesTo investigate the interaction between heat and ambient air pollution on daily mortality in a large dataset of 620 cities from 36 countries. MethodsWe used daily data on all-cause mortality, air temperature, particulate matter ≤ 10 μm (PM10), PM ≤ 2.5 μm (PM2.5), nitrogen dioxide (NO2), and ozone (O3) from 620 cities in 36 countries in the period 1995–2020. We restricted the analysis to the six consecutive warmest months in each city. City-specific data were analysed with over-dispersed Poisson regression models, followed by a multilevel random-effects meta-analysis. The joint association between air temperature and air pollutants was modelled with product terms between non-linear functions for air temperature and linear functions for air pollutants. ResultsWe analyzed 22,630,598 deaths. An increase in mean temperature from the 75th to the 99th percentile of city-specific distributions was associated with an average 8.9 % (95 % confidence interval: 7.1 %, 10.7 %) mortality increment, ranging between 5.3 % (3.8 %, 6.9 %) and 12.8 % (8.7 %, 17.0 %), when daily PM10 was equal to 10 or 90 μg/m3, respectively. Corresponding estimates when daily O3 concentrations were 40 or 160 μg/m3 were 2.9 % (1.1 %, 4.7 %) and 12.5 % (6.9 %, 18.5 %), respectively. Similarly, a 10 μg/m3 increment in PM10 was associated with a 0.54 % (0.10 %, 0.98 %) and 1.21 % (0.69 %, 1.72 %) increase in mortality when daily air temperature was set to the 1st and 99th city-specific percentiles, respectively. Corresponding mortality estimate for O3 across these temperature percentiles were 0.00 % (-0.44 %, 0.44 %) and 0.53 % (0.38 %, 0.68 %). Similar effect modification results, although slightly weaker, were found for PM2.5 and NO2. ConclusionsSuggestive evidence of effect modification between air temperature and air pollutants on mortality during the warm period was found in a global dataset of 620 cities.