An improved meteorological variables-based aerosol optical depth estimation method by combining a physical mechanism model with a two-stage model.

A two-stage model integrating a spatiotemporal linear mixed effect (STLME) and a geographic weight regression (GWR) model is proposed to improve the meteorological variables-based aerosol optical depth (AOD) retrieval method (Elterman retrieval model-ERM). The proposed model is referred to as the STG-ERM model. The STG-ERM model is applied over the Beijing-Tianjin-Hebei (BTH) region in China for the years 2019 and 2020. The results show that data coverage increased by 39.0% in 2019 and 40.5% in 2020. Cross-validation of the retrieval results versus multi-angle implementation of atmospheric correction (MAIAC) AOD shows the substantial improvement of the STG-ERM model over earlier meteorological models for AOD estimation, with a determination coefficient (R2) of daily AOD of 0.86, root mean squared prediction error (RMSE) and the relative prediction error (RPE) of 0.10 and 36.14% in 2019 and R2 of 0.86, RMSE of 0.12 and RPE of 37.86% in 2020. The fused annual mean AOD indicates strong spatial variation with high value in south plain and low value in northwestern mountainous areas of the BTH region. The overall spatial seasonal mean AOD ranges from 0.441 to 0.586, demonstrating strongly seasonal variation. The coverage of STG-ERM retrieved AOD, as determined in this exercise by leaving out part of the meteorological data, affects the accuracy of fused AOD. The coverage of the meteorological data has smaller impact on the fused AOD in the districts with low annual mean AOD of less than 0.35 than that in the districts with high annual mean AOD of greater than 0.6. If available, continuous daily meteorological data with high spatiotemporal resolution can improve the model performance and the accuracy of fused AOD. The STG-ERM model may serve as a valuable approach to provide data to fill gaps in satellite-retrieved AOD products.

Comparative Analysis of Meteorological versus In Situ Variables in Ship Thermal Simulations.

Thermal simulations have become increasingly popular in assessing energy efficiency and predicting thermal behaviors in various structures. Calibration of these simulations is essential for accurate predictions. A crucial aspect of this calibration involves investigating the influence of meteorological variables. This study aims to explore the impact of meteorological variables on thermal simulations, particularly focusing on ships. Using TRNSYS (TRaNsient System Simulation) software (v17), renowned for its capability to model complex energy systems within buildings, the significance of incorporating meteorological data into thermal simulations was analyzed. The investigation centered on a patrol vessel stationed in a port in Galicia, northwest Spain. To ensure accuracy, we not only utilized the vessel's dimensions but also conducted in situ temperature measurements onboard. Furthermore, a dedicated weather station was installed to capture real-time meteorological data. Data from multiple sources, including Meteonorm and MeteoGalicia, were collected for comparative analysis. By juxtaposing simulations based on meteorological variables against those relying solely on in situ measurements, we sought to discern the relative merits of each approach in enhancing the fidelity of thermal simulations.

Leaf chlorophyll content is the crucial factor for the temporal and spatial variation of global plants leaf maximum carboxylation rate.

Photosynthesis plays an important role in the terrestrial carbon and water cycles which are often studied using terrestrial biosphere models (TBMs). The maximum carboxylation rate at 25 °C (Vcmax25) is a key parameter in the photosynthesis module of TBMs, yet the spatiotemporal distribution of Vcmax25 and the driving mechanism are not fully understood. In this study, Enzyme Kinetics response model, leaf chlorophyll content response model and partial correlation analysis were used to analyze the temporal and spatial changes patterns of atmospheric environment, enzyme dynamic and soil nutrition on Vcmax25 and the driving mechanism, and has made a few useful conclusions: (1) Vcmax25 varies significantly with latitude and between- and within-plant function types (PFTs), which mainly dependent on leaf chlorophyll content (LCC). Under the influence of temperature, the contribution of LCC to the seasonal variation of Vcmax25 is very different among the eight main biomes, with an average contribution of 21 %. (2) The relationship between meteorological variables and Vcmax25 was significant, due to the fact that meteorological variables drive the Rubisco enzyme content that have a significant relationship with Vcmax25, rather than directly acting on Vcmax25. (3) Soil nutrient elements had significant influence on the spatiotemporal variation of Vcmax25 and LCC. The results showed that soil total carbon, soil nitrogen and organic carbon not only affect the temporal and spatial pattern of Vcmax25, but also are the key factors of LCC temporal-spatial variation. These findings provide useful information for better parameterization of Vcmax25 in TBMs.

Characterization of environmental drivers influencing the abundance of Anopheles maculipennis complex in Northern Italy.

BACKGROUND: In Italy, malaria was endemic until the 1970s, when it was declared eradicated by WHO. Nowadays, with the persistence of competent mosquito populations, the effect of climate change, and increased possibility of importing malaria parasites from endemic counties due to growing migration, a malaria resurgence in Italy has become more likely. Hence, enhancing the understanding of the current distribution of the Anopheles maculipennis complex and the factors that influence the presence of this malaria vector is crucial, especially in Northern Italy, characterised by a high density of both human population and livestock. METHODS: To assess the presence and abundance of malaria vectors, a 4-year field survey in the plain areas of Lombardy and Emilia-Romagna region in Italy was conducted. Every sampling point was characterised in space by the land use in a 500-m radius and in time considering meteorological data collected in the short and long time periods before sampling. We combined the results of a linear regression model with a random forest analysis to understand the relative importance of the investigated niche dimensions in determining Anopheles mosquito presence and abundance. RESULTS: The estimated normalised variable importance indicates that rice fields were the most important land use class explaining the presence of Anopheles, followed by transitional woodlands and shrubland. Farm buildings were the third variable in terms of importance, likely because of the presence of animal shelters, followed by urbanised land. The two most important meteorological variables influencing the abundance of Anopheles in our study area were mean temperature in the 24 h before the sampling date and the sum of degree-days with temperature between 18 °C and 30 °C in the 14 days before the sampling date. CONCLUSIONS: The results obtained in this study could be helpful in predicting the risk of autochthonous malaria transmission, based on local information on land cover classes that might facilitate the presence of malaria vectors and presence of short- and medium-term meteorological conditions favourable to mosquito development and activity. The results can support the design of vector control measures through environmental management.

Revealing causality in the associations between meteorological variables and air pollutant concentrations.

Understanding the role of meteorology in determining air pollutant concentrations is an important goal for better comprehension of air pollution dispersion and fate. It requires estimating the strength of the causal associations between all the relevant meteorological variables and the pollutant concentrations. Unfortunately, many of the meteorological variables are not routinely observed. Furthermore, the common analysis methods cannot establish causality. Here we use the output of a numerical weather prediction model as a proxy for real meteorological data, and study the causal relationships between a large suite of its meteorological variables, including some rarely observed ones, and the corresponding nitrogen dioxide (NO2) concentrations at multiple observation locations. Time-lagged convergent cross mapping analysis is used to ascertain causality and its strength, and the Pearson and Spearman correlations are used to study the direction of the associations. The solar radiation, temperature lapse rate, boundary layer height, horizontal wind speed and wind shear were found to be causally associated with the NO2 concentrations, with mean time lags of their maximal impact at -3, -1, -2 and -3 hours, respectively. The nature of the association with the vertical wind speed was found to be uncertain and region-dependent. No causal association was found with relative humidity, temperature and precipitation.

Application of roadside air purifiers in urban street canyons: A pilot-scale study in Hong Kong.

The implementation of roadside air purifiers has emerged as an effective active control measure to alleviate air pollution in urban street canyons. However, technical questions raised under real conditions remain challenging. In this study, we conducted a pilot-scale investigation involving seven units of self-designed roadside air purifiers in an urban street canyon in Hong Kong. The air cleaning effects were quantified with an air quality sensor network after rigorous quality control. The removal efficiencies of Nitrogen dioxide (NO2), Fine suspended particulates (PM2.5), Carbon monoxide (CO), and Nitric oxide (NO) were determined by comparing with simultaneously measured ambient concentrations, with hourly average efficiencies of 14.0 %-16.9 %, 3.5-10.0 %, 11.9 %-18.7 %, and 19.2 %-44.9 %, respectively. Generally, the purification effects presented variations depending on the ambient pollutants' levels. Higher ambient concentrations of NO2, PM2.5, CO correlated with increased purification effects, while NO presented the opposite trend. The influence of interval distance combined with spatial distribution indicated the operation of purifiers will induce local NO2 attenuation even at an interval distance of four meters. Statistical analysis delivered evidence the air cleaning ability exhibited optimal performance when relative humidity level is ranged from 70 % to 90 %, aligning with the prevailing conditions in Hong Kong. Additionally, improved purification effects were observed at the downwind direction, and their performance was enhanced when the wind speed exceeded 2.5 m/s. Moreover, we estimated the operational lifetime of the air purifiers to be approximately 130 days, offering crucial information regarding the filter replacement cycle. This work serves as a pioneering case study, showcasing the feasibility and deployment considerations of roadside air purifiers in effectively controlling air pollution in urban environments.

Sero-molecular epidemiology of Japanese encephalitis virus in swine population of western Uttar Pradesh, India: Unraveling the geographical expansion of the virus.

BACKGROUND & OBJECTIVES: Swine is a good sentinel for forecast of Japanese encephalitis virus (JEV) outbreaks in humans. The present study was envisaged with objectives to know the sero-conversion period of JEV and to assess the prevalence of JEV in swine population of western Uttar Pradesh state of India. METHODS: A total of 252 swine serum samples were screened using IgM ELISA over the period of one year to determine the sero-conversion rate and compared seasonally to check the transmission peak of virus. Further, 321 swine blood and serum samples were collected from all seven divisions of western Uttar Pradesh to determine prevalence of JEV using real time RT-PCR and ELISA. RESULTS: Seasonal sero-conversion rate was high during monsoon and post-monsoon (32%) followed by winter (22.91%) and summer (10.71%) seasons. The sero-conversion was observed in all months indicating viral activity throughout the year in the region. The low degree of correlation was found between meteorological variables (day temperature, rainfall) and sero-conversion rate. A total of 52 samples (16.19%) were found positive by real time RT-PCR while sero-positivity of 29.91% was observed using IgG and IgM ELISA(s). The overall prevalence of JEV was 39.25%. INTERPRETATION & CONCLUSION: The presence of JEV was recorded throughout the year with peak occurrence during monsoon and post-monsoon season indicating that virus has spread its realm to western region of the state. The information generated in the present study will aid in initiating timely vector control measures and human vaccination program to mitigate risk of JEV infection in the region.

Particulate matter 10 µm (PM10), 2.5 µm (PM2.5) datasets gathered by direct measurement, low-cost sensor and by public air quality stations in Fontibón, Bogotá D.C., Colombia.

Concentration of particulate matter directly affects air quality and human health. Three sources of information were used in this work to generate datasets on this matter at the Fontibón county in Bogota D.C., Colombia. The first source was a Davis AirLinkⓇ low-cost sensor air quality readings for PM2.5, PM10 and meteorological variables. The sensor was installed in the referred area, collecting air quality readings for PM2.5, PM10, as well as temperature, relative humidity, dew point, wet bulb, and heat index as meteorological variables during the months of May to August 2022. The second source was collecting by direct measurement the PM10 particles using a TischⓇ Hi- Vol equipment, evaluated the concentration of particulate matter PM10 in the same place for 27 days. Finally, raw data was provided by the Bogotá's Environmental District Bureau (SDA), validating in this work the data readings for the years 2021 and 2022 from the two meteorological stations located in the same county, named "Fontibón" and "Móvil Fontibón", including Air quality data for PM2.5, PM10, Carbon Monoxide (CO), Ozone, Nitrogen Dioxide (NO2), Sulfur Dioxide (SO2) and the meteorological variables wind speed, wind direction, temperature, precipitation, relative humidity (RH) and Barometric pressure. A Machine Learning model was made to perform the mining and completeness of the missing data with an iterative imputation and with a regression model, and the Pearson, Spearman and Kendall correlation coefficients were calculated, using Python language.

Analyzing the relationship between air temperature and respiratory morbidity in children and the elderly in Porto Alegre, Brazil, before and during the COVID-19 pandemic.

The aim of the study was to analyze the relationship between air temperature data against hospital admissions due to respiratory diseases of children (under five years of age) and the elderly (over 65) in subtropical Porto Alegre, Brazil, comparing outcomes for 3 sequential years, 2018-2020, pre- and post-COVID 19 pandemic. Meteorological and hospital admission (HA) data for Porto Alegre, marked by a Koeppen-Geiger's Cfa climate type with well-defined seasons, were used in the analyses. HA was obtained for respiratory diseases (J00-99, according to the International Classification of Diseases, ICD-10) from the Brazilian DATASUS (Unified Health System database). We performed correlation analysis between variables (HA versus air temperature and heat stress) in order to identify existing relationships and lag effects (between meteorological condition and morbidity). Relative risk (RR) was also obtained for the two age groups during the three years. Results showed that the pandemic year disrupted observed patterns of association between analyzed variables, with either very low or non-existent correlations.

Machine learning to predict foodborne salmonellosis outbreaks based on genome characteristics and meteorological trends.

Several studies have shown a correlation between outbreaks of Salmonella enterica and meteorological trends, especially related to temperature and precipitation. Additionally, current studies based on outbreaks are performed on data for the species Salmonella enterica, without considering its intra-species and genetic heterogeneity. In this study, we analyzed the effect of differential gene expression and a suite of meteorological factors on salmonellosis outbreak scale (typified by case numbers) using a combination of machine learning and count-based modeling methods. Elastic Net regularization model was used to identify significant genes from a Salmonella pan-genome, and a multi-variable Poisson regression developed to fit the individual and mixed effects data. The best-fit Elastic Net model (α = 0.50; λ = 2.18) identified 53 significant gene features. The final multi-variable Poisson regression model (χ2 = 5748.22; pseudo R2 = 0.669; probability > χ2 = 0) identified 127 significant predictor terms (p < 0.10), comprising 45 gene-only predictors, average temperature, average precipitation, and average snowfall, and 79 gene-meteorological interaction terms. The significant genes ranged in functionality from cellular signaling and transport, virulence, metabolism, and stress response, and included gene variables not considered as significant by the baseline model. This study presents a holistic approach towards evaluating multiple data sources (such as genomic and environmental data) to predict outbreak scale, which could help in revising the estimates for human health risk.

Random forest regression on joint role of meteorological variables, demographic factors, and policy response measures in COVID-19 daily cases: global analysis in different climate zones.

Different sources of factors in environment can affect the spread of COVID-19 by influencing the diffusion of the virus transmission, but the collective influence of which has hardly been considered. This study aimed to utilize a machine learning algorithm to assess the joint effects of meteorological variables, demographic factors, and government response measures on COVID-19 daily cases globally at city level. Random forest regression models showed that population density was the most crucial determinant for COVID-19 transmission, followed by meteorological variables and response measures. Ultraviolet radiation and temperature dominated meteorological factors, but the associations with daily cases varied across different climate zones. Policy response measures have lag effect in containing the epidemic development, and the pandemic was more effectively contained with stricter response measures implemented, but the generalized measures might not be applicable to all climate conditions. This study explored the roles of demographic factors, meteorological variables, and policy response measures in the transmission of COVID-19, and provided evidence for policymakers that the design of appropriate policies for prevention and preparedness of future pandemics should be based on local climate conditions, population characteristics, and social activity characteristics. Future work should focus on discerning the interactions between numerous factors affecting COVID-19 transmission.

Climate and human health: a review of publication trends in the International Journal of Biometeorology.

The climate-health nexus is well documented in the field of biometeorology. Since its inception, Biometeorology has in many ways become the umbrella under which much of this collaborative research has been conducted. Whilst a range of review papers have considered the development of biometeorological research and its coverage in this journal, and a few have reviewed the literature on specific diseases, none have focused on the sub-field of climate and health as a whole. Since its first issue in 1957, the International Journal of Biometeorology has published a total of 2183 papers that broadly consider human health and its relationship with climate. In this review, we identify a total of 180 (8.3%, n = 2183) of these papers that specifically focus on the intersection between meteorological variables and specific, named diagnosable diseases, and explore the publication trends thereof. The number of publications on climate and health in the journal increases considerably since 2011. The largest number of publications on the topic was in 2017 (18) followed by 2021 (17). Of the 180 studies conducted, respiratory diseases accounted for 37.2% of the publications, cardiovascular disease 17%, and cerebrovascular disease 11.1%. The literature on climate and health in the journal is dominated by studies from the global North, with a particular focus on Asia and Europe. Only 2.2% and 8.3% of these studies explore empirical evidence from the African continent and South America respectively. These findings highlight the importance of continued research on climate and human health, especially in low- and lower-middle-income countries, the populations of which are more vulnerable to climate-sensitive illnesses.

A hybrid model for estimating the number concentration of ultrafine particles based on machine learning algorithms in central Taiwan.

Modeling is a cost-effective measure to estimate ultrafine particle (UFP) levels. Previous UFP estimates generally relied on land-use regression with insufficient temporal resolution. We carried out in-situ measurements for UFP in central Taiwan and developed a model incorporating satellite-based measurements, meteorological variables, and land-use data to estimate daily UFP levels at a 1-km resolution. Two sampling campaigns were conducted for measuring hourly UFP concentrations at six sites between 2008-2010 and 2017-2021, respectively, using scanning mobility particle sizers. Three machine learning algorithms, namely random forest, eXtreme gradient boosting (XGBoost), and deep neural network, were used to develop UFP estimation models. The performances were evaluated with a 10-fold cross-validation, temporal, and spatial validation. A total of 1,022 effective sampling days were conducted. The XGBoost model had the best performance with a training coefficient of determination (R2) of 0.99 [normalized root mean square error (nRMSE): 6.52%] and a cross-validation R2 of 0.78 (nRMSE: 31.0%). The ten most important variables were surface pressure, distance to the nearest road, temperature, calendar year, day of the year, NO2, meridional wind, the total length of roads, PM2.5, and zonal wind. The UFP levels were elevated along the main roads across different seasons, suggesting that traffic emission is an important contributor to UFP. This hybrid model outperformed prior land use regression models and thus can provide more accurate estimates of UFP for epidemiological studies.

Causal relationship between outdoor atmospheric quality and pediatric asthma visits in hangzhou.

Many air pollutants and climate variables have proven to be significantly associated with pediatric asthma and have worsened asthma symptoms. However, their exact causal effects remain unclear. We explored the causality between air pollutants, climate, and daily pediatric asthma patient visits with a short-term lag effect. Based on eight years of daily environmental data and daily pediatric asthma patient visits, Spearman correlation analysis was used to select the air pollutants and climate variables that correlated with daily pediatric asthma patient visits at any time (with a lag of 1-6 days). We regarded these environmental variables as treatments and built multiple- and single-treatment causal inference models using the Dowhy library (a Python library for causal inference by graphing the model, quantitatively evaluating causal effects, and validating the causal assumptions) to estimate the quantitative causal effect between these correlated variables and daily pediatric asthma patient visits in lag time. The multiple-treatment causal inference model was a model with 8 treatments (Visibility, Precipitation, PM10, PM2.5, SO2, NO2, AQI and CO), 1 outcome (daily pediatric asthma patients visits), and 5 confounders (Humidity, Temperature, Sea level pressure, wind speed and unobserved confounders "U"). Single-treatment causal inference models were 8 models, and each model has 1 treatment, 1 outcome and 12 confounders. Spearman correlation analysis showed that precipitation, wind speed, visibility, air quality index, PM2.5, PM10, SO2, NO2, and CO were significantly associated variables at all times (p < 0.05). The multiple-treatment model showed that pooled treatments had significant causality for the short-term lag (lag1-lag6; p < 0.05). Causality was mainly due to SO2. In the single-treatment models, visibility, SO2, NO2, and CO exhibited significant causal effects at any one time (p < 0.05). SO2 and CO exhibited stronger positive causal effects. The causal effect of SO2 reached its maxima (causal effect = 11.41, p < 0.05) at lag5. The greatest causal effect of CO appeared at lag3 (causal effect = 10.67, p < 0.05). During the eight year-period, the improvements in SO2, CO, and NO2 in Hangzhou were estimated to reduce asthma visits by 8478.03, 3131.08, and 1341.39 per year, respectively. SO2, NO2, CO, and visibility exhibited causal effects on daily pediatric asthma patient visits; SO2 was the most crucial causative variable with a relatively higher causal effect, followed by CO. Improvements in atmospheric quality in the Hangzhou area have effectively reduced the incidence of asthma.

Evaluation of MODIS combined DT and DB AOD retrievals and their association with meteorological variables over Qena, Egypt.

The purpose of this study is to validate the daily Terra-MODIS level 2 combined dark target (DT) and deep blue (DB) aerosol optical depth (AOD) retrievals with a spatial resolution of 10 km against the ground-based AERONET AOD data to be used in evaluating the air pollution and impact of meteorological variables over Qena, Egypt, in 2019. The regression analysis demonstrated an accepted agreement between the MODIS and AERONET AOD data with a correlation coefficient (R) of 0.7118 and 74.22% of the collocated points fall within the expected error (EE) limits. Quality flag filtering and spatial and temporal collocation were found to have a significant impact on the regression results. Quality flag filtering increased R by 0.2091 and % within EE by 17.97, spatial collocation increased R by 0.0143 and % within EE by 1.13, and temporal collocation increased R by 0.0089 and % within EE by 4.43. By validating the MODIS AOD data seasonally and analyzing the temporal distribution of the seasonal AOD data to show the retrieval accuracy variations between seasons, it was found that the MODIS AOD observations overestimated the AERONET AOD values in all seasons, and this may be because of underestimating the surface reflectance. Perhaps the main reason for the highest overestimation in summer and autumn is the transportation of aerosols from other regions, which changes the aerosol model in Qena, making accurate aerosol-type assumptions more difficult. Therefore, this study recommends necessary improvements regarding the aerosol model selection and the surface reflectance calculations. Temperature and relative humidity were found to have a strong negative relationship with a correlation of - 0.735, and both have a moderate association with AOD with a correlation of 0.451 and - 0.356, respectively. Because Qena is not a rainy city, precipitation was found to have no correlation with the other variables.

Associations between short-term temperature exposure and kidney-related conditions in New York State: The influence of temperature metrics across four dimensions.

BACKGROUND: Evidence describing the relationship between short-term temperature exposure and kidney-related conditions is insufficient. It remains unclear how temperature specification affects estimation of these associations. This study aimed to assess associations between short-term temperature exposure and seven kidney-related conditions and to evaluate the influence of temperature specification. METHODS: We obtained data on hospital encounters in New York State (2007-2016). We assessed associations with a case-crossover design using conditional logistic regression with distributed lag non-linear models. We compared model performance (i.e., AIC) and association curves using 1) five temperature spatial resolutions; 2) temperature on an absolute versus relative scale; 3) seven temperature metrics incorporating humidity, wind speed, and/or solar radiation; and 4) five intraday temperature measures (e.g., daily minimum and daytime mean). RESULTS: We included 1,209,934 unplanned adult encounters. Temperature metric and intraday measure had considerably greater influence than spatial resolution and temperature scale. For outcomes not associated with temperature exposure, almost all metrics or intraday measures showed good model performance; for outcomes associated with temperature, there were meaningful differences in performance across metrics or intraday measures. For parsimony, we modelled daytime mean outdoor wet-bulb globe temperature, which showed good performance for all outcomes. At lag 0-6 days, we observed increased risk at the 95th percentile of temperature versus the minimum morbidity temperature for acute kidney failure (odds ratio [OR] = 1.36, 95% confidence interval [CI]: 1.09, 1.69), urolithiasis (OR = 1.41, 95% CI: 1.16, 1.70), dysnatremia (OR = 1.26, 95% CI: 1.01, 1.59), and volume depletion (OR = 1.88, 95% CI: 1.41, 2.51), but not for glomerular diseases, renal tubulo-interstitial diseases, and chronic kidney disease. CONCLUSIONS: High-temperature exposure over one week is a risk factor for acute kidney failure, urolithiasis, dysnatremia, and volume depletion. The differential model performance across temperature metrics and intraday measures indicates the importance of careful selection of exposure metrics when estimating temperature-related health burden.

Short-term impact of noise, other air pollutants and meteorological factors on emergency hospital mental health admissions in the Madrid region.

BACKGROUND: A number of environmental factors, such as air pollution, noise in urbanised settings and meteorological-type variables, may give rise to important effects on human health. In recent years, many studies have confirmed the relation between various mental disorders and these factors, with a possible impact on the increase in emergency hospital admissions due to these causes. The aim of this study was to analyse the impact of a range of environmental factors on daily emergency hospital admissions due to mental disorders in the Madrid Autonomous Region (MAR), across the period 2013-2018. METHODOLOGY: Longitudinal ecological time series study analysed by Generalised Linear Models with Poisson regression, with the dependent variable being daily Emergency Hospital Mental Health Admissions (EHMHA) in the MAR, and the independent variable being mean daily concentrations of chemical pollutants, noise levels and meteorological variables. RESULTS: EHMHA were related statistically significantly in the short term with diurnal noise levels. Relative risks (RRs) for total admissions due to mental disorders and self-inflicted injuries, in the case of diurnal noise was RR: 1.008 95%CI (1.003 1.013). Admissions attributable to diurnal noise account for 5.5% of total admissions across the study period. There was no association between hospital admissions and chemical air pollution. CONCLUSION: Noise is a variable that shows a statistically significant short-term association with EHMHA across all age groups in the MAR region. The results of this study may serve as a basis for drawing up public health guidelines and plans, which regard these variables as risk factors for mental disorders, especially in the case of noise, since this fundamentally depends on anthropogenic activities in highly urbanised areas with high levels of traffic density.

Effects of Ambient Temperature, Relative Humidity, and Precipitation on Diarrhea Incidence in Surabaya.

BACKGROUND: Diarrhea remains a common infectious disease caused by various risk factors in developing countries. This study investigated the incidence rate and temporal associations between diarrhea and meteorological determinants in five regions of Surabaya, Indonesia. METHOD: Monthly diarrhea records from local governmental health facilities in Surabaya and monthly means of weather variables, including average temperature, precipitation, and relative humidity from Meteorology, Climatology, and Geophysical Agency were collected from January 2018 to September 2020. The generalized additive model was employed to quantify the time lag association between diarrhea risk and extremely low (5th percentile) and high (95th percentile) monthly weather variations in the north, central, west, south, and east regions of Surabaya (lag of 0-2 months). RESULT: The average incidence rate for diarrhea was 11.4 per 100,000 during the study period, with a higher incidence during rainy season (November to March) and in East Surabaya. This study showed that the weather condition with the lowest diarrhea risks varied with the region. The diarrhea risks were associated with extremely low and high temperatures, with the highest RR of 5.39 (95% CI 4.61, 6.17) in the east region, with 1 month of lag time following the extreme temperatures. Extremely low relative humidity increased the diarrhea risks in some regions of Surabaya, with the highest risk in the west region at lag 0 (RR = 2.13 (95% CI 1.79, 2.47)). Extremely high precipitation significantly affects the risk of diarrhea in the central region, at 0 months of lag time, with an RR of 3.05 (95% CI 2.09, 4.01). CONCLUSION: This study identified a high incidence of diarrhea in the rainy season and in the deficient developed regions of Surabaya, providing evidence that weather magnifies the adverse effects of inadequate environmental sanitation. This study suggests the local environmental and health sectors codevelop a weather-based early warning system and improve local sanitation practices as prevention measures in response to increasing risks of infectious diseases.

Meteorological, PM2.5 and PM10 factors on SARS-COV-2 transmission: The case of southern regions in Chile.

There are several determinants of a population's health, including meteorological factors and air pollution. For example, it is well known that low temperatures and air pollution increase mortality rates in infant and elderly populations. With the emergence of SARS-COV-2, it is important to understand what factors contribute to its mitigation and control. There is some research in this area which shows scientific evidence on the virus's behavior in the face of these variables. This research aims to quantify the impact of climatic factors and environmental pollution on SARS-COV-2 specifically the effect on the number of new infections in different areas of Chile. At the local level, historical information available from the Department of Statistics and Health Information, the Chilean National Air Quality Information System, the Chilean Meteorological Directorate, and other databases will allow the generation of panel data suitable for the analysis. The results show the significant effect of pollution and climate variables measured in lags and will allow us to explain the behavior of the pandemic by identifying the relevant factors affecting health, using heteroskedastic models, which in turn will serve as a contribution to the generation of more effective and timely public policies for the control of the pandemic.

Air pollution and meteorological variables' effects on COVID-19 first and second waves in Spain.

The aim of this research is to study the influence of atmospheric pollutants and meteorological variables on the incidence rate of COVID-19 and the rate of hospital admissions due to COVID-19 during the first and second waves in nine Spanish provinces. Numerous studies analyze the effect of environmental and pollution variables separately, but few that include them in the same analysis together, and even fewer that compare their effects between the first and second waves of the virus. This study was conducted in nine of 52 Spanish provinces, using generalized linear models with Poisson link between levels of PM10, NO2 and O3 (independent variables) and maximum temperature and absolute humidity and the rates of incidence and hospital admissions of COVID-19 (dependent variables), establishing a series of significant lags. Using the estimators obtained from the significant multivariate models, the relative risks associated with these variables were calculated for increases of 10 µg/m3 for pollutants, 1 °C for temperature and 1 g/m3 for humidity. The results suggest that NO2 has a greater association than the other air pollution variables and the meteorological variables. There was a greater association with O3 in the first wave and with NO2 in the second. Pollutants showed a homogeneous distribution across the country. We conclude that, compared to other air pollutants and meteorological variables, NO2 is a protagonist that may modulate the incidence and severity of COVID-19, though preventive public health measures such as masking and hand washing are still very important. Supplementary Information: The online version contains supplementary material available at 10.1007/s13762-022-04190-z.