In-vehicle exposure to NO2 and PM2.5:A comprehensive assessment of controlling parameters and reduction strategies to minimise personal exposure

Vehicles are the third most occupied microenvironment, other than home and workplace, in developed urban areas. Vehicle cabins are confined spaces where occupants can mitigate their exposure to on-road nitrogen dioxide (NO2) and fine particulate matter (PM2.5) concentrations. Understanding which parameters exert the greatest influence on in-vehicle exposure underpins advice to drivers and vehicle occupants in general. This study assessed the in-vehicle NO2 and PM2.5 levels and developed stepwise general additive mixed models (sGAMM) to investigate comprehensively the combined and individual influences of factors that influence the in-vehicle exposures. The mean in-vehicle levels were 19 ± 18 and 6.4 ± 2.7 μg/m3 for NO2 and PM2.5, respectively. sGAMM model identified significant factors explaining a large fraction of in-vehicle NO2 and PM2.5 variability, R2 = 0.645 and 0.723, respectively. From the model's explained variability on-road air pollution was the most important predictor accounting for 22.3 and 30 % of NO2 and PM2.5 variability, respectively. Vehicle-based predictors included manufacturing year, cabin size, odometer reading, type of cabin filter, ventilation fan speed power, window setting, and use of air recirculation, and together explained 48.7 % and 61.3 % of NO2 and PM2.5 variability, respectively, with 41.4 % and 51.9 %, related to ventilation preference and type of filtration media, respectively. Driving-based parameters included driving speed, traffic conditions, traffic lights, roundabouts, and following high emitters and accounted for 22 and 7.4 % of in-vehicle NO2 and PM2.5 exposure variability, respectively. Vehicle occupants can significantly reduce their in-vehicle exposure by moderating vehicle ventilation settings and by choosing an appropriate cabin air filter

NO2 levels inside vehicle cabins with pollen and activated carbon filters::A real world targeted intervention to estimate NO2 exposure reduction potential

Traffic related nitrogen dioxide (NO2) poses a serious environmental and health risk factor in the urban environment. Drivers and vehicle occupants in general may have acute exposure to NO2 levels. In order to identify key controllable measures to reduce vehicle occupant's exposure, this study measures NO2 exposure inside ten different vehicles under real world driving conditions and applies a targeted intervention by replacing previously used filters with new standard pollen and new activated carbon cabin filters. The study also evaluates the efficiency of the latter as a function of duration of use. The mean in-vehicle NO2 exposure across the tested vehicles, driving the same route under comparable traffic and ambient air quality conditions, was 50.8 ± 32.7 μg/m3 for the new standard pollen filter tests and 9.2 ± 8.6 μg/m3 for the new activated carbon filter tests. When implementing the new activated carbon filters, overall we observed significant (p < 0.05) reductions by 87 % on average (range 80 - 94.2 %) in the in-vehicle NO2 levels compared to the on-road concentrations. We further found that the activated carbon filter NO2 removal efficiency drops by 6.8 ± 0.6 % per month; showing a faster decay in removal efficiency after the first 6 months of use. These results offer novel insights into how the general population can control and reduce their exposure to traffic related NO2. The use and regular replacement of activated carbon cabin air filters represents a relatively inexpensive method to significantly reduce in-vehicle NO2 exposure

High temperature sensitivity of monoterpene emissions from global vegetation

AbstractTerrestrial vegetation emits vast amounts of monoterpenes into the atmosphere, influencing ecological interactions and atmospheric chemistry. Global emissions are simulated as a function of temperature with a fixed exponential relationship (β coefficient) across forest ecosystems and environmental conditions. We applied meta-analysis algorithms on 40 years of published monoterpene emission data and show that relationship between emissions and temperature is more sensitive and intricate than previously thought. Considering the entire dataset, a higher temperature sensitivity (β = 0.13 ± 0.01 °C−1) is derived but with a linear increase with the reported coefficients of determination (R2), indicating that co-occurring environmental factors modify the temperature sensitivity of the emissions that is primarily related to the specific plant functional type (PFT). Implementing a PFT-dependent β in a biogenic emission model, coupled with a chemistry – climate model, demonstrated that atmospheric processes are exceptionally dependent on monoterpene emissions which are subject to amplified variations under rising temperatures.</jats:p

Predicting real-time within-vehicle air pollution exposure with mass-balance and machine learning approaches using on-road and air quality data

Modelling the air pollutant concentrations within-vehicles is an essential step to estimate our daily exposure to air pollution. This is a challenging issue however, since the processes that affect the exposures within-vehicles change with different driving patterns and ventilation settings. This study introduces an innovative approach that combines mass-balance principles and machine learning techniques, leveraging ambient air quality, on-road and within-vehicle measurements of particulate matter (PM10, PM2.5, PM1), nitrogen dioxide (NO2), nitrogen oxides (NOx), aerosol lung surface deposited area (LSDA) and ultrafine particles (UFP) under different ventilation settings to estimate air pollution exposure levels within vehicles. The first model (MB) includes basic physical and chemical processes and follows a mass-balance approach to estimate the within-vehicle concentrations. The second model (ML) applies data driven machine learning algorithms to a training set of observations to predict unseen within-vehicle concentrations. By using a number generator, the whole observational dataset was divided to 80:20 and 80% was used to build and train the ML model, while 20% was used for validation. Both models demonstrated good predictions of observations apart from an underestimation in UFP and LSDA. The ML model showed better predictive power than the MB model and had skill in predicting the unseen within-vehicle exposures. The ML model predictions were as good as the MB model for most of the species and improved for NO2. The ML model demonstrated good index of agreement (IOA >0.69) and Pearson correlation coefficient (r > 0.80) for all the species. The inclusion of air quality data from nearby monitoring stations instead of on-road (sampled while driving), in the ML model showed promising and new capabilities to within-vehicle exposure predictions. In an era where air pollution is a growing concern, understanding and predicting within-vehicle air pollution exposure is of great importance for public health and environmental research. This research not only advances the field of exposure assessment but (at no extra cost) also demonstrates practical implications for real-time exposure mapping and health impact assessment of vehicle occupants with existing infrastructure

High temperature sensitivity of monoterpene emissions from global vegetation

Terrestrial vegetation emits vast amounts of monoterpenes into the atmosphere, influencing ecological interactions and atmospheric chemistry. Global emissions are simulated as a function of temperature with a fixed exponential relationship (β coefficient) across forest ecosystems and environmental conditions. We applied meta-analysis algorithms on 40 years of published monoterpene emission data and show that relationship between emissions and temperature is more sensitive and intricate than previously thought. Considering the entire dataset, a higher temperature sensitivity (β = 0.13 ± 0.01 °C−1 ) is derived but with a linear increase with the reported coefficients of determination (R2), indicating that co-occurring environmental factors modify the temperature sensitivity of the emissions that is primarily related to the specific plant functional type (PFT). Implementing a PFT-dependent β in a biogenic emission model, coupled with a chemistry - climate model, demonstrated that atmospheric processes are exceptionally dependent on monoterpene emissions which are subject to amplified variations under rising temperatures