Air, Volume 2, Issue 1 (March 2024) – 5 articles

Airports are significant contributors of atmospheric pollutant aerosols, namely ultrafine particles (UFPs). This study characterizes the particle number concentration (PNC), the median particle size (d_mn50), and the metallic composition of medium-haul area and engine aerosols at two French airports (Paris-CDG and Marseille). This study followed the standard operating procedures for characterizing aerosol emissions from 5 nm to 8 μm (OECD, 2015; EN 17058:2018). It allows determining which are the specific parameters directly related to the emission sources and their contribution to the overall aerosols measured at workplace in airports. The particulate emissions observed during aircraft engine start-up were ~19× higher than the average airborne concentration. The particle size distributions remained mostly <250 nm with d_mn50 < 100 nm (showing a specificity for the medium-haul area with an average d_mn50 of ~12 nm). The d_mn50 can be used to distinguish emission peaks due to aircrafts (d_mn50~15 nm) from those due to apron vehicle activities (d_mn50 > 20 nm). Chemical elements (titanium and zinc) were identified as potential tracers of aircraft emissions and occurred mainly at the micrometric scale. For aircraft engine emissions, UFPs are mainly due to fuel combustion with the presence of carbon/oxygen. The study concludes with suggestions for future research to extend on the findings presented. Full article

Biomass burning is one of the largest sources of particulate matter emissions globally. However, the emission of particulate inorganic species from prescribed grassland burning in Japan has not yet been characterized. In this study, we collected total suspended particulate matter from prescribed grassland burning in Hirado, Akiyoshidai, and Aso, Japan. The collected filter samples were brought to the laboratory, and water-soluble inorganic components were analyzed via ion chromatography. The measurement results showed high excess concentrations of potassium, calcium, and magnesium, and these substances were highly correlated, which agreed with previously reported findings. In contrast, the concentrations of sodium, chloride, nitrate, and sulfate were insignificant, even though their high concentrations were reported in other biomass burning studies. Among these low concentration substances, a high correlation was still observed between sulfate and nitrate. It is possible that the low concentrations of those species could have been biased in the measurements, particularly as a result of subtracting blank and background values from the observed concentrations. Building up more data in this area may allow us to characterize the significance of domestic biomass burning’s contribution to inorganic particulate components in Japanese air, which may consequently contributes to better understanding of adverse health effect of airborne particulate matter. Full article

This paper describes an innovative method that recursively applies the machine learning Random Forest to an assumed homogeneous aerographic domain around measurement sites to predict concentrations and emissions of ammonia, an atmospheric pollutant that causes acidification and eutrophication of soil and water and contributes to secondary PM_2.5. The methodology was implemented to understand the effects of weather and emission changes on atmospheric ammonia concentrations. The model was trained and tested by hourly measurements of ammonia concentrations and atmospheric turbulence parameters, starting from a constant emission scenario. The initial values of emissions were calculated based on a bottom-up emission inventory detailed at the municipal level and considering a circular area of about 4 km radius centered on measurement sites. By comparing predicted and measured concentrations for each iteration, the emissions were modified, the model’s training and testing were repeated, and the model converged to a very high performance in predicting ammonia concentrations and establishing hourly time-varying emission profiles. The ammonia concentration predictions were extremely accurate and reliable compared to the measured values. The relationship between NH₃ concentrations and the calculated emissions rates is compatible with physical atmospheric turbulence parameters. The site-specific emissions profiles, estimated by the proposed methodology, clearly show a nonlinear relation with measured concentrations and allow the identification of the effect of atmospheric turbulence on pollutant accumulation. The proposed methodology is suitable for validating and confirming emission time series and defining highly accurate emission profiles for the improvement of the performances of chemical and transport models (CTMs) in combination with in situ measurements and/or optical depth from satellite observation. Full article

Background: Growing evidence suggests that air pollution may influence fetal development, with potential consequences for later health. Alteration of telomere length (TL) is one possible mediating mechanism for the link between fetal exposure to air pollution and the development of disease. However, the few studies exploring associations between prenatal pollution and infant TL have assessed varied trimesters of pregnancy and shown mixed results. The aim of this study was to examine the differential relationships between prenatal exposure to air pollutant PM_2.5 during the first, second, and third trimesters of pregnancy with infant TL at one month of age. Methods: Women (n = 74) were recruited in obstetric clinics during their third trimester. Data on PM_2.5 exposure for each woman’s residential area during each trimester was acquired from the regional Air Quality Management District. At one month postnatal, a salivary sample was collected from the infant, which provided DNA for the telomere assay. Women completed questionnaires about stressors in their lives, perceived stress, depression, and sociodemographics for inclusion as covariates. Multiple linear regression was used to analyze the results. Results: PM_2.5 exposure during the second (β = 0.31, p = 0.003) and third (β = 0.24, p = 0.02) trimesters was associated with longer infant TL. Exposure in the first trimester was not related to TL. Covariates of maternal depression and age and infant female sex were also associated with longer TL. Variables in the model contributed to 34% of the variance in TL (F = 10.58, p = 0.000). Discussion: Fetal programming of longer telomeres in response to pollution may have adaptive value in preparing the neonate for a postnatal environment that is less than optimal in terms of air quality. Alternatively, longer telomeres may forecast later health risks, considering established links between longer TL and diseases such as cancer. Future research needs to address how prenatal pollution interacts with TL to influence health over time. Full article

The global concern of escalating ambient air pollution and its profound impact on human health is paramount. While traffic police personnel are critical for maintaining the road safety and transportation system of any city in India, they are susceptible to occupational health risks due to ambient air pollution. This study investigated health challenges faced by traffic police personnel due to prolonged exposure to air pollutants prevalent in traffic-congested areas, including particulate matter (PM_2.5 and PM₁₀), nitrogen dioxide, and sulfur dioxide. The first phase of this study collected and analyzed secondary air quality data over five years (2019–2023) across six locations in Kolkata, India. The second phase employed a questionnaire-based survey to assess the health implications of air pollution exposure. The survey questionnaire captured information on physical health symptoms, stress-related indicators, lifestyle habits, and work hours of around 100 police personnel from Kolkata with indoor (control group) and outdoor (exposed group) work responsibilities. The results of this study established a strong positive correlation between air pollution and a range of health issues experienced by the exposed group. The outcome of this study is significant for urban planning, policy formulation, and public health interventions geared toward minimizing the adverse impacts of air pollution on traffic police personnel. Full article