A methodological framework for estimating ambient PM2.5 particulate matter concentrations in the UK.

Scientific evidence sustains PM2.5 particles' inhalation may generate harmful impacts on human beings' health; therefore, their monitoring in ambient air is of paramount relevance in terms of public health. Due to the limited number of fixed stations within the air quality monitoring networks, development of methodological frameworks to model ambient air PM2.5 particles is primordial to providing additional information on PM2.5 exposure and its trends. In this sense, this work aims to offer a global easily-applicable tool to estimate ambient air PM2.5 as a function of meteorological conditions using a multivariate analysis. Daily PM2.5 data measured by 84 fixed monitoring stations and meteorological data from ERA5 (ECMWF Reanalysis v5) reanalysis daily based data between 2000 and 2021 across the United Kingdom were attended to develop the suggested approach. Data from January 2017 to December 2020 were employed to build a mathematical expression that related the dependent variable (PM2.5) to predictor ones (sea-level pressure, planetary boundary layer height, temperature, precipitation, wind direction and speed), while 2021 data tested the model. Evaluation indicators evidenced a good performance of model (maximum values of RMSE, MAE and MAPE: 1.80 µg/m3, 3.24 µg/m3, and 20.63%, respectively), compiling the current legislation's requirements for modelling ambient air PM2.5 concentrations. A retrospective analysis of meteorological features allowed estimating ambient air PM2.5 concentrations from 2000 to 2021. The highest PM2.5 concentrations relapsed in the Mid- and Southlands, while Northlands sustained the lowest concentrations.

The short-term association between environmental variables and daily pediatric asthma patient visits in Hangzhou, China: A time-series study.

Background: To date, a large number of studies have shown correlations between environmental variables and pediatric asthma in short-term lag time. However, their results are inconsistent. Therefore, we aimed to evaluate the short-term impact of environmental variables on daily pediatric asthma patients' visits (DPAPV) in Hangzhou, China, and find the most important risk factor. Methods: Generalized additive distribution lag non-linear model (GAM-DLNM) was applied to explore the effect of environmental variables on DPAPV in single- and multi-variable models in Hangzhou, China from 2014 to 2021. Then, risk factors of pediatric asthma were selected (p < 0.05 both in single- and multi-variable models) and used weighted quantile sum (WQS) regression model to evaluate their relative importance. Results: There were 313,296 pediatric asthma patient records between 2014 and 2021. Both in single- and multi-variable models, PM2.5, PM10, and NO2 exhibited significant positive correlations in short-term lag time and these correlations reached their maximum in lag day 2 (RR = 1.00, 95%CI:1.00 to 1.01), lag day 2 (RR = 1.00, 95%CI:1.00 to 1.01), and lag day 3 (RR = 1.04, 95%CI:1.02 to1.05), respectively. The WQS index showed that NO2 had the greatest relative importance (weight over 70 %). The relative importance of NO2 increased with time passing. Males were more susceptible to the adverse effects of NO2. Conclusion: PM2.5, PM10, and NO2 had significant adverse effects on pediatric asthma. Among them, NO2 presented the greatest and most important adverse effect on the disease. Therefore, parents could give priority to paying attention to NO2 to control children's asthma.

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.

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.