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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.

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Chinese diesel trucks are the main contributors to NOx and particulate matter (PM) vehicle emissions. An increase in diesel trucks could aggravate air pollution and damage human health. The Chinese government has recently implemented a series of emission control technologies and measures for air quality improvement. This paper summarizes recent control technologies and measures for diesel truck emissions in China and introduces the comprehensive application of control technologies and measures in Beijing-Tianjin-Hebei and surrounding regions. Remote online monitoring technology has been adopted according to the China VI standard for heavy-duty diesel trucks, and control measures such as transportation structure adjustment and heavy pollution enterprise classification control continue to support the battle action plan for pollution control. Perspectives and suggestions are provided for promoting pollution control and supervision of diesel truck emissions: adhere to the concept of overall management and control, vigorously promote the application of systematic and technological means in emission monitoring, continuously facilitate cargo transportation structure adjustment and promote new energy freight vehicles. This paper aims to accelerate the implementation of control technologies and measures throughout China. China is endeavouring to control diesel truck exhaust pollution. China is willing to cooperate with the world to protect the global ecological environment.

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BACKGROUND: Traffic-related air pollution (TRAP) is a major contributor to urban pollution and varies sharply at the street level, posing a challenge for air quality modeling. Traditional land use regression models combined with data from fixed monitoring stations may be unable to predict and characterize fine-scale TRAP, especially in complex urban environments influenced by various features. This study aims to estimate fine-scale (50 m) concentrations of nitrogen oxides (NO and NO2) in Hong Kong using a deep learning (DL) structured model. METHODS: We collected data from mobile air quality sensors on buses and crowd-sourced Google real-time traffic status as a proxy for real-time traffic emissions. Our DL model was compared with existing machine learning models to assess performance improvements. Using an interpretable machine learning method, we hierarchically evaluated the global, local, and interaction effects for different features. RESULTS: Our DL model outperformed existing machine learning models, achieving R2 values of 0.72 for NO and 0.69 for NO2. The incorporation of traffic status as a key predictor improved model performance by 9% to 17%. The interpretable machine learning method revealed the importance of traffic-related features and their pairwise interactions. CONCLUSION: The results indicate that traffic-related features significantly contribute to TRAP and provide insights and guidance for urban planning. By incorporating crowd-sourced Google traffic information, we assessed traffic abatement scenarios that could inform targeted strategies for improving urban air quality.

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Indoor air quality (IAQ) is increasingly recognised as one of the critical factors influencing human health, particularly given the amount of time people spend indoors. This study investigated the impact of real-life kitchen human activity (KHA) on IAQ. We used low-cost sensors to measure real-time concentrations of smoke, carbon monoxide (CO), and particulate matter (PM10 and PM2.5) in the kitchen of a household with three adults, analysing KHAs by dividing them into five categories. The fixed effect model was employed to analyse the data, explaining the impact of different KHAs on IAQ. The results showed that compared to other KHAs, using the gas stove had the greatest impact on IAQ, with average increases of 13% in smoke, 24.4% in CO, 9.8% in PM10, and 5.34% in PM2.5. The study also found that without windows and with insufficient ventilation, only using the range hood cannot effectively and obviously reduce PM levels. These findings highlight the need for comprehensive IAQ management strategies and further research. Despite its limitations, this study also validated the potential of low-cost sensors in IAQ monitoring.

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Large-scale climate indicators (LSCI) refer to the intricate connections between the atmosphere, oceans, and continents in specific regions. To comprehend the relationship between these vital indicators and atmospheric and climate variability, it is crucial to explore them in detail. The objective of the present study is to gather and review relevant research on LSCI in the Mediterranean area to gain a better understanding of their impacts on atmospheric variability, climate, air quality, ecosystems, and health in the region. Numerous studies have explored LSCI and their effects in the study area, and our work aims to contribute to the existing literature in this context. Our study concludes that LSCI are linked to spatial atmospheric variability in the Mediterranean region. They influence the spatial and temporal distribution of climate and environmental variability, including temperature, rainfall, extreme events, cyclones and storms, and air pollution. Some studies have demonstrated the effects of LSCI on ecosystems, such as forests and river basins in the region. However, research on their impacts on human health is limited. Additionally, the application of LSCI involves various formulations and explanations of their potential developments, primarily explaining atmospheric complex systems and the effort required to comprehend their implications for the environment and health. This review highlights recent progress made in defining, formulating, and calculating LSCI in the Mediterranean area. The most critical functions and characteristics of LSCI are also discussed. Understanding LSCI and their applications is the first step towards developing a health warning system, starting with monitoring atmospheric dynamics and culminating in managing human health responses.

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It has been widely acknowledged that high temperatures and heatwaves promote ozone concentration, worsening the ambient air quality. However, temperature can impact ozone via multiple pathways, and quantifying each path is challenging due to environmental confounders. In this study, we frame the problem as a treatment-outcome issue and utilize a machine learning-aided causal inference technique to disentangle the impact of temperature on ozone formation. Our approach reveals that failing to account for the covariations of solar radiation and other meteorological factors leads to an overestimation of the O3-temperature response. Through process evaluation, we find that temperature influences local ozone formation mainly by accelerating chemical reactions and enhancing precursor production and changing boundary layer heights. The O3 response to temperature via enhancing soil NOx and changing relative humidity and wind field is however observable. A better appreciation of O3-temperature response is critical for improving air quality regulation in the warming future.

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China and India are two of the fastest-growing developing economies covering about 35 % of the world's population. Due to the extensive prevalence of air pollution across cities in China and India, contemporary assessment of atmospheric pollution through real-time and remote sensing observations is inadequate. The study aims to determine the spatial distribution and temporal variation of hazardous atmospheric pollutants across cities in China (Shanghai, Nanjing, Jinan, Zhengzhou and Beijing) and India (Kolkata, Asansol, Patna, Kanpur and Delhi). Ground observation data on CO, O3, PM2.5, PM10, NO2 and SO2 along with remote sensing data on AOD, CO, O3, BC, NO2, SO2 and dust surface mass concentrations are used to assess atmospheric pollution. This study examines daily, zonal and longitudinal pollutant distributions using Sentinel-5 P data and surface mass concentrations over the vertical column evaluated from NASA satellite data. The Mann-Kendall test and relative change methods have been implemented to assess pollutant trends while Sen's Slope identifies the magnitude of change. The similarity test and data validation methods including NRMSE, PC and MBias have been employed to ensure consistency in analysing annual trends for each air pollutant in the datasets. Additionally, multiple correlation matrix analysis has been used to examine the associations among different pollutants from both datasets based on their annual averages. Remote sensing data reveals that eastern China and north-eastern India have the highest aerosol, BC, CO, NO2 and SO2 while western China and southern India lowest. Dust peaks in the west while O3 levels are highest in the northern part of China and India. Ground observation data indicates that Chinese cities have higher annual mean SO2 and O3 concentrations with yearly declines in PM2.5, PM10, NO2, SO2 and CO notably SO2. Indian cities witnessed overall increases in PM2.5, PM10, NO2 and SO2 from 2012 to 2019 with a slight decline in 2020 followed by a resurgence in 2023. The findings provide insights for implementing regional policy measures to reduce air pollution based on changes in pollutant behaviour. The study suggests that addressing atmospheric pollutants, particularly NO2, CO, PM2.5, PM10, and SO2 requires a comprehensive environmental policy framework involving central and state governments and enforcing stringent environmental protection laws.

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This study presents a temporal evaluation of the tropospheric NO2 column densities over Greater Doha using TROPOMI satellite data from May 2018 to December 2023, and an assessment of the impact of the preparations and hosting of the FIFA Football World Cup Qatar 2022, on NO2 levels before, during and after the tournament over Greater Doha. Analysis of annual NO2 levels from 2019 to 2023 showed an increase in 2022 compared to that of the previous three years and a clear decrease in 2023 post the completion of the world cup preparations and hosting. Results also showed an increase in NO2 levels during winter compared to that in summer, with wind speed being an important determining factor. Findings showed that Fridays and Saturdays (both constitute the local weekend in Qatar) were 44% and 13% lower than that of the averaged weekdays, respectively. The annual NO2 levels in the post-world cup year of 2023 were found to be 24% lower than that in 2022 and around 16% lower than that of the previous years. NO2 levels during the World Cup tournament (20 Nov to Dec 18, 2022) were found to be higher than that of the same corresponding periods in all other available years including an increase of 27% compared to that in 2023. Wind speed played an important role in determining the NO2 levels during the world cup period and accounted for >96% of their daily variability, indicating that meteorological factors substantially influenced the NO2 column during the event.

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In the context of the air quality co-benefits of carbon neutrality, conventional strategies for the end-of-pipe control reduction of volatile organic compounds (VOCs) towards carbon dioxide (CO2) need to be revised more realistically. This study explored the synergetic removal of carbonyls with low carbon emission by amine-functionalized manganese dioxide (MnO2), obtained with a method involving freezing-thawing cycles. Molecular-level characterization revealed that an ordered array of interfacial water dimers (H5O2+, a class of water-proton clusters) on the MnO2 surface enhanced the robust bonding of metal sites with amino groups. Amine-functionalized MnO2 can be negatively charged under environmental acidity to further interfacial proton-coupled electron transfers. Cooperativity in the interfacial chemical processes facilitated the selective conversion of carbonyl carbons to bicarbonated amides (NH3+HCO3-) as a reservoir of CO2. Compared with a commercially used 2,4-dinitrophenylhydrazine (DNPH) control, the nearly complete removal of a priority carbonyl mixture containing formaldehyde, acetaldehyde, and acetone was attained synergically. The secondary organic compounds in the gas phase and CO2 off-gas were suppressed.

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Fine particulate matter 2.5 (PM2.5) is a widely studied pollutant with substantial health impacts, yet little is known about the urban-rural differences across the United States. Trends of PM2.5 in urban and rural census tracts between 2010 and 2019 were assessed alongside sociodemographic characteristics including race/ethnicity, poverty, and age. For 2010, we identified 13,474 rural tracts and 59,065 urban tracts. In 2019, 13,462 were rural and 59,055 urban. Urban tracts had significantly higher PM2.5 concentrations than rural tracts during this period. Levels of PM2.5 were lower in rural tracts compared to urban and fell more rapidly in rural than urban. Rural tract annual means for 2010 and 2019 were 8.51 [2.24] µg/m3 and 6.41 [1.29] µg/m3, respectively. Urban tract annual means for 2010 and 2019 were 9.56 [2.04] µg/m3 and 7.51 [1.40] µg/m3, respectively. Rural and urban majority Black communities had significantly higher PM2.5 pollution levels (10.19 [1.64] µg/m3 and 9.79 [1.10] µg/m3 respectively), in 2010. In 2019, they were: 7.75 [1.1] µg/m3 and 7.09 [0.78] µg/m3, respectively. Majority Hispanic communities had higher PM2.5 levels and were the highest urban concentration among all races/ethnicities (8.01 [1.73] µg/m3), however they were not the highest rural concentration among all races/ethnicities (6.22 [1.60] µg/m3) in 2019. Associations with higher levels of PM2.5 were found with communities in the poorest quartile and with higher proportions of residents age<15 years old. These findings suggest greater protections for those disproportionately exposed to PM2.5 are needed, such as, increasing the availability of low-cost air quality monitors.

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Air quality in airport attracts a widespread attention due to the emission of GHGs and pollutants related with aircraft flight. Sustainable aviation fuel (SAF) has confirmed PM2.5 reduction due to free of aromatics and sulphur, and thus air quality improvement in airport is prospected by SAF blend. Two types of SAF were assessed the potential of energy saving and emission reduction by ZF850 jet engine. FT fuel is characterized with only paraffins without aromatics and cycloparaffins while HCHJ fuels is characterized with no aromatics. The descend of air quality and SAF blend were both investigated the effect on the engine performance and emission characteristic. The critical parameters were extracted from fuel compositions and air pollutants. Ambient air with a higher PM2.5 could lead to the rise of engine emission especially in UHC and PM2.5 despite at the low thrust setting and high thrust setting, and even couple with 3.2% rise in energy consumption and 1% reduction in combustion efficiency. CO, NO and NO2 in ambient air show less influence on engine performance and emission characteristic than PM2.5. Both types of SAF blend were observed significant reductions in PM2.5 and UHC. PM2.5 reduction obtained 37.9%-99.8% by FT blend and 0.64%-93.9% by HCHJ blend through the whole trust settings. There are almost 6.67% positive benefit in TSFC through the whole thrust setting by 7% FT blend. The effects of air quality and SAF blend on engine emission present significant changes on PM and UHC but the slight change on CO and NOx. By SAF blend, the energy saving and pollutant reduction obtained could be both benefit for air quality improvement in airport and further reduce engine emission as the feedback of less pollutants in ambient air.

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Actual acquired air quality time series data are highly volatile and nonstationary, and accurately predicting nonlinear time series data containing complex noise is an ongoing challenge. This paper proposes an air quality prediction method based on empirical mode decomposition (EMD), a transformer and a bidirectional long short-term memory neural network (BiLSTM), which is good at addressing the ultrashort-term prediction of nonlinear time-series data and shows good performance for application to the air quality dataset of Patna, India (6:00 am on October 3, 2015-0:00 pm on July 1, 2020). The AQI sequence is first decomposed into intrinsic mode functions (IMFs) via EMD and subsequently predicted separately via the improved transformer algorithm based on BiLSTM, where linear prediction is performed for IMFs with simple trends. Finally, the predicted values of each IMF are integrated using BiLSTM to obtain the predicted AQI values. This paper predicts the AQI in Patna with a time window of 5 h, and the RMSE, MAE and MAPE are as low as 5.6853, 2.8230 and 2.23%, respectively. Moreover, the scalability of the proposed model is validated on air quality datasets from several other cities, and the results prove that the proposed hybrid model has high performance and broad application prospects in real-time air quality prediction.

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Smart cities rely on a network of sensors to gather real-time data on various environmental factors, including air quality. This paper addresses the challenges of improving the accuracy of low-cost particulate matter sensors (LCPMSs) which can be compromised by environmental conditions, such as high humidity, which is common in many urban areas. Such weather conditions often lead to the overestimation of particle counts due to hygroscopic particle growth, resulting in a potential public concern, although most of the detected particles consist of just water. The paper presents an innovative design for an indicative air-quality measuring station that integrates the particulate matter sensor with a preconditioning subsystem designed to mitigate the impact of humidity. The preconditioning subsystem works by heating the incoming air, effectively reducing the relative humidity and preventing the hygroscopic growth of particles before they reach the sensor. To validate the effectiveness of this approach, parallel measurements were conducted using both preconditioned and non-preconditioned sensors over a period of 19 weeks. The data were analyzed to compare the performance of the sensors in terms of accuracy for PM1, PM2.5, and PM10 particles. The results demonstrated a significant improvement in measurement accuracy for the preconditioned sensor, especially in environments with high relative humidity. When the conditions were too severe and both sensors started measuring incorrect values, the preconditioned sensor-measured values were closer to the actual values. Also, the period of measuring incorrect values was shorter with the preconditioned sensor. The results suggest that the implementation of air preconditioning subsystems in LCPMSs deployed in smart cities can provide a cost-effective solution to overcome humidity-related inaccuracies, thereby improving the overall quality of measured air pollution data.

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This study presents a fit-for-purpose lab and field evaluation of commercially available portable sensor systems for PM, NO2, and/or BC. The main aim of the study is to identify portable sensor systems that are capable of reliably quantifying dynamic exposure gradients in urban environments. After an initial literature and market study resulting in 39 sensor systems, 10 sensor systems were ultimately purchased and benchmarked under laboratory and real-word conditions. We evaluated the comparability to reference analyzers, sensor precision, and sensitivity towards environmental confounders (temperature, humidity, and O3). Moreover, we evaluated if the sensor accuracy can be improved by applying a lab or field calibration. Because the targeted application of the sensor systems under evaluation is mobile monitoring, we conducted a mobile field test in an urban environment to evaluate the GPS accuracy and potential impacts from vibrations on the resulting sensor signals. Results of the considered sensor systems indicate that out-of-the-box performance is relatively good for PM (R2 = 0.68-0.9, Uexp = 16-66%, BSU = 0.1-0.7 µg/m3) and BC (R2 = 0.82-0.83), but maturity of the tested NO2 sensors is still low (R2 = 0.38-0.55, Uexp = 111-614%) and additional efforts are needed in terms of signal noise and calibration, as proven by the performance after multilinear calibration (R2 = 0.75-0.83, Uexp = 37-44%)). The horizontal accuracy of the built-in GPS was generally good, achieving <10 m accuracy for all sensor systems. More accurate and dynamic exposure assessments in contemporary urban environments are crucial to study real-world exposure of individuals and the resulting impacts on potential health endpoints. A greater availability of mobile monitoring systems capable of quantifying urban pollutant gradients will further boost this line of research.

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Recent advances in sensor technology for air pollution monitoring open new possibilities in the field of environmental epidemiology. The low spatial resolution of fixed outdoor measurement stations and modelling uncertainties currently limit the understanding of personal exposure. In this context, air quality sensor systems (AQSSs) offer significant potential to enhance personal exposure assessment. A pilot study was conducted to investigate the feasibility of the NO2 sensor model B43F and the particulate matter (PM) sensor model OPC-R1, both from Alphasense (UK), for use in epidemiological studies. Seven patients with chronic obstructive pulmonary disease (COPD) or asthma had built-for-purpose sensor systems placed inside and outside of their homes at fixed locations for one month. Participants documented their indoor activities, presence in the house, window status, and symptom severity and performed a peak expiratory flow test. The potential inhaled doses of PM2.5 and NO2 were calculated using different data sources such as outdoor data from air quality monitoring stations, indoor data from AQSSs, and generic inhalation rates (IR) or activity-specific IR. Moreover, the relation between indoor and outdoor air quality obtained with AQSSs, an indoor source apportionment study, and an evaluation of the suitability of the AQSS data for studying the relationship between air quality and health were investigated. The results highlight the value of the sensor data and the importance of monitoring indoor air quality and activity patterns to avoid exposure misclassification. The use of AQSSs at fixed locations shows promise for larger-scale and/or long-term epidemiological studies.

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Accurate air quality forecasting is crucial for public health, environmental monitoring and protection, and urban planning. However, existing methods fail to effectively utilize multi-scale information, both spatially and temporally. There is a lack of integration between individual monitoring stations and city-wide scales. Temporally, the periodic nature of air quality variations is often overlooked or inadequately considered. To overcome these limitations, we conduct a thorough analysis of the data and tasks, integrating spatio-temporal multi-scale domain knowledge. We present a novel Multi-spatial Multi-temporal air quality forecasting method based on Graph Convolutional Networks and Gated Recurrent Units (M2G2), bridging the gap in air quality forecasting across spatial and temporal scales. The proposed framework consists of two modules: Multi-scale Spatial GCN (MS-GCN) for spatial information fusion and Multi-scale Temporal GRU (MT-GRU) for temporal information integration. In the spatial dimension, the MS-GCN module employs a bidirectional learnable structure and a residual structure, enabling comprehensive information exchange between individual monitoring stations and the city-scale graph. Regarding the temporal dimension, the MT-GRU module adaptively combines information from different temporal scales through parallel hidden states. Leveraging meteorological indicators and four air quality indicators, we present comprehensive comparative analyses and ablation experiments, showcasing the higher accuracy of M2G2 in comparison to nine currently available advanced approaches across all aspects. The improvements of M2G2 over the second-best method on RMSE of 72-h future predictions are as follows: PM2.5: 6%∼10%; PM10: 5%∼7%; NO2: 5%∼16%; O3: 6%∼9%. Furthermore, we demonstrate the effectiveness of each module of M2G2 by ablation study. We conduct a sensitivity analysis of air quality and meteorological data, finding that the introduction of O3 adversely impacts the prediction accuracy of PM2.5.

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OBJECTIVE: Weather conditions such as low air temperatures, low barometric pressure, and low wind speed have been linked to more cases of carbon monoxide (CO) poisoning. However, limited literature exists regarding the impact of air pollution. This study aims to investigate the relationship between outdoor air pollution and CO poisoning in 2 distinct cities in Turkey. METHODS: A prospective study was conducted at 2 tertiary hospitals, recording demographic data, presenting complaints, vital signs, blood gas and laboratory parameters, carboxyhemoglobin (COHb) levels, meteorological parameters, and pollutant parameters. Complications and outcomes were also documented. RESULTS: The study included 83 patients (Group 1 = 44, Group 2 = 39). The air quality index (AQI) in Group 2 (61.7 ± 27.7) (moderate AQI) was statistically significantly higher (dirtier AQI) than that in Group 1 (47.3 ± 26.4) (good AQI) (P = 0.018). The AQI was identified as an independent predictor for forecasting the need for hospitalization (OR = 1.192, 95% CI: 1.036 - 1.372, P = 0.014) and predicting the risk of developing cardiac complications (OR: 1.060, 95% CI: 1.017 - 1.104, P = 0.005). CONCLUSIONS: The AQI, derived from the calculation of 6 primary air pollutants, can effectively predict the likelihood of hospitalization and cardiac involvement in patients presenting to the emergency department with CO poisoning.

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Although significant progress has been made in controlling emissions from stationary combustion sources in China over the past decade, understanding of condensable particulate matter (CPM) emissions from these sources and their impact on ambient PM2.5 remains limited. In this study, we established the source profiles and emission inventories of CPM from coal-fired industrial boilers (CFIBs), coal-fired power plants (CFPPs), and iron and steel industry (ISIs) for the Yangtze River Delta (YRD) region of China; furthermore, the air quality model (Community Multiscale Air Quality, CMAQ) was used to evaluate the impact of their CPM emissions from these three types of stationary combustion sources on ambient PM2.5 during Feb. 2018, a month characterized by elevated PM2.5 concentrations. The results indicated that CPM emissions from these three sources in the YRD region before and after the implementation of the ultra-low emissions (ULE) policy amounted to 109,839 and 43,338 tons, respectively, with particularly high emission intensity along the Yangtze River. The implementation of CFPPs ULE policy was shown to reduce the impact of CPM emissions from the three stationary sources on monthly PM2.5 concentrations from 0.92 µg/m3 to 0.41 µg/m3 (with a maximum of 5.35 µg/m3), a reduction that exceeded the decrease of 0.31 µg/m3 in PM2.5 concentrations resulting from the emission reductions of conventional pollutants (FPM, SO2 and NOx). CPM emissions from the three stationary sources were found to increase the PM2.5 by 0.68 µg/m3 during pollution periods. The largest components of PM2.5 contributed by CPM emissions from stationary combustion sources were sulfate, organic carbon, and nitrate, accounting for 21.4 %, 21.1 %, and 18.2 %, respectively. Particularly, CPM's contributions to PM2.5 varied by altitude, with a relatively large impact at altitudes between 220 and 460 m. Attention should be given to CPM emission control, with particular priority placed on implementing ULE measures for ISIs and CFIBs.

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Background: With the increasing prevalence of wildfire smoke in the Pacific Northwest, it is important to quantify health impacts to plan for adequate health services. The Rogue Valley region has historically faced some of the greatest wildfire threats in the state. Health impacts from smoke have been estimated in several recent studies that include Oregon's Rogue Valley, but the results between studies are conflicting. Objective: The objective is to critically examine impacts of wildfire smoke on health in the Rogue Valley area and translate the results to support hospital staffing decisions. Study design: The study adopts a case-crossover approach. Methods: Apply a conditional Poisson regression to analyze time stratified counts while controlling for mean temperature. Results: Every 10 µ/m3 increase in PM2.5 is associated with a 2% increase in same-day hospital or emergency room admission rates for respiratory conditions during fire season after adjusting for temperature and time (OR = 1.020; 95% CI: 1.004-1.034); a 10 µ/m3 increase in PM2.5 lasting nine days is associated with a 4% increase in admission rates (OR = 1.041; 95% CI: 1.018-1.065). In other words, for each 10 µ/m3 single day increase in pollution from smoke, an additional 0.26 respiratory patients would be expected in the area hospitals. With a single day increase from 10 µ/m3 to 150 µ/m3, hospitals could expect an additional four patients. Conclusions: There are small but significant health impacts in the Rogue Valley. These impacts are smaller than some statewide estimates. We need further research to understand these differences.

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Objectives: This research aimed to examine the connection between indoor air quality and respiratory function in preschool children, a topic that, to the best of our knowledge, has not been explored before. Methods: This cross-sectional study was conducted within the geographical location of the Ministry of Education of Jeddah. Four hundred preschool-aged children (4-6 years old) from four preschools were enrolled. Structured questionnaires and peak flow meter (PFM) were used to assess the overall health and pulmonary function of the participants. An air detector for formaldehyde (HCHO), volatile organic compound, and fine particulate dust matter and a carbon dioxide (CO2) detector with temperature and humidity monitors were used to measure the air pollutants. Results: A significant difference was observed in PFM measurement between the four preschools (P = 0.017). The highest PFM green zone value was identified in the North preschool (n = 32, 54.2%), and the lowest value was identified in the Central preschool (n = 21, 33.3%). Regarding the red zone, the highest value was observed in the Central preschool (n = 14, 22.2%) and the lowest in the North preschool (n = 1, 1.7%). PFM measurement in the green zone showed lower CO2 levels (P = 0.014) and temperature (P = 0.04) than those in the yellow and red zones. Conclusion: Children schooling in adequate ventilation environments had better respiratory function than those in inadequate environmental ventilation.