Air Quality Atmosphere and Health最新文献

Skin exposome encapsulates all internal and environmental exposures that affect skin health. Of these, photo-pollution refers to the combined effect on human skin of the simultaneous exposure to solar radiation (especially UV) and air pollution. Providing personalised photo-pollution exposure warnings and dose monitoring to an individual through a smartphone app could help in reducing skin ageing and degradation as well as in managing skin conditions (for example Atopic Dermatitis). However, accurate monitoring is challenging without a potentially expensive or cumbersome sensor device. In this work we present an innovative satellite-based air pollutant monitoring software service, ExpoPol, developed by siHealth Ltd. ExpoPol synthesises several inputs including live satellite imagery in real-time into an artificial intelligence (AI) model to provide assessment of the exposure of a smartphone user to relevant air pollutants, such as nitrogen oxides (NO_x), poly-aromatic hydrocarbons (PAH) and ozone (O₃). When combined with siHealth’s patented technology HappySun® for solar radiation monitoring, ExpoPol can effectively provide a sensor-less personal skin photo-pollution dosimetry. By downscaling satellite data using local geographic data, ExpoPol is capable of monitoring pollutants with street-level resolution and global coverage in near real-time. We evaluate the accuracy of ExpoPol against ground-station monitoring data for three pollutants across three continental regions (Europe, Asia, North America) and find R² values of 0.62, 0.65, 0.74 for PM₁₀, PM_2.5, NO₂ respectively. ExpoPol is shown to be significantly more accurate than a state-of-the-art global atmospheric forecasting system (CAMS) over the same ground-station dataset and provide data on much finer spatial resolutions. The presented system can support the real-time automatic assessment of the user’s skin exposome, anywhere and anytime. This paves the way for the development of mobile applications empowering users and clinicians to make informed decisions about skin health, or assisting dermocosmetic manufacturers in the creation of personalised products for personal care (e.g., skin ageing prevention or hair care).

Emission of microplastics (MP) to the atmosphere, airborne transport, and subsequent deposition are now recognized. However, the temporal and spatial resolution of data on MP pollution and knowledge of their atmospheric behaviour and fate is still very limited. Hence, we investigated MP wet and dry deposition in Central Germany and examined the role of weather conditions on MP contamination levels. Monthly samples of dry and wet deposition were taken over an eight-month period (05/2019-12/2019) and analysed by micro-Fourier-Transform Infrared spectroscopy (µFTIR) down to 11 μm particle size and one dry deposition sample was subjected to Raman analysis to determine plastic particles down to a size of 0.5 μm. MP in a size range from 11 μm to 130 μm were detected in all wet deposition samples and in 4 out of 8 dry deposition samples by µFTIR. Polypropylene particles were found most frequently and accounted for 62% and 54% of all particles in wet and dry deposition samples, respectively. Over the eight-month period, wet deposition of MP slightly dominated at the study site and comprised 59% of the total MP deposition. The MP mean total (wet + dry) deposition flux (DF) was 17 ± 14 MP m^− 2 day^− 1. Extensive Raman analyses of an exemplary dry deposition sample revealed additional plastic particles in the extended size range from 1 to 10 μm resulting in a deposition flux of 207 MP m^− 2 day^− 1. Our results suggest that MP analysis by µFTIR down to 11 μm may underestimate DF at least by an order of magnitude. More comprehensive studies on submicron plastics and nanoplastics are needed to fully assess air pollution by plastic particles.

Due to developing aviation sector, number of aircraft in the world is increasing. Along with this development, problems such as the decrease in air quality in and around the airport also arise. In this study, it is tried to calculate pollutant emissions occurring in 2022 during the LTO cycles of Denizli Çardak Airport in Turkey. These calculations are based on the information obtained from ICAO Engine Emission Data Bank and flight information published by the General Directorate of State Airports Authority (GDSAA). As a result of the data obtained, 74.64 ton/year pollutants (NOx-37.148 t/y, CO-35.398 t/y and HC-2.094 t/y) were calculated for 2022 at Denizli Çardak Airport. Of all emissions, NOx accounted for 50%, CO 47% and HC 3%. In the LTO cycle, the most fuel is burned in taxi cycle and pollutant emissions produced in this cycle are greater. With a 2 min reduction in taxi time, there will be an approximate 6.8% reduction in the total emission rate in the LTO cycle. Similarly, with a 4 min reduction in taxi time, there will be a 13.72% reduction in the whole emission rate in the LTO cycle. Unlike other studies, in this study the emission rates of various engines were compared. It has been calculated that the amount of pollutant emissions produced by the new generation Boeing 737 MAX LEAP-1B powered aircraft in LTO cycle is 25% less than the amount of pollutant emissions produced by the Airbus A320 NEO LEAP-1 A powered aircraft. The biggest factor here is that the emission of CO pollutants is less. Considering the emission rates produced by these four different engines (B737-800 CFM56-7B, A320 V2500-A1, B737 MAX LEAP-1B, A320 NEO LEAP-1 A), the Airbus A320 V2500-A1 engine is a more environmentally friendly engine than the other engines.

Mathematical models serve as crucial tools for quantitatively assessing the environmental and population impact resulting from the release of hazardous substances. Often, precise source parameters remain elusive, leading to a reliance on rudimentary assumptions. This challenge is particularly pronounced in scenarios involving releases that are accidental or deliberate acts of terrorism. A conventional method for estimating the source term involves the construction of backward plumes originating from various sensors measuring tracer concentrations. The area displaying the highest overlap of these backward plumes typically offers an initial approximation for the most probable release location. The backward plume (BP) method has been compared with a machine learning based method. Both methods use data from a field campaign and from a synthetic dataset built from a simple setup featuring receptors arranged linearly downwind from the release point. A substantial number (~ 1500) of forward plume simulations are conducted, each initiated from random locations and under varying meteorological conditions. This extensive dataset encompasses critical meteorological variables and concentration measurements recorded by idealized receptors. Subsequently, the dataset has been partitioned into training and testing subsets. A feed-forward neural network (NN) has been employed. This NN is trained using the concentration data from the receptors and the associated meteorological variables as input, with the source location coordinates serving as the output. Subsequent verification is carried out using the testing dataset, facilitating a comparison between the NN's and BP’s predictions and the actual source locations. One of the key advantages of the NN-based approach is its ability to rapidly estimate the source term, typically within a fraction of a second on a standard laptop. This speed is of paramount significance in scenarios involving accidental releases, where swift response is essential. Notably, the computationally intensive tasks of dataset construction and NN training can be conducted offline, providing preparedness in areas where accidental releases may be anticipated.

Gas chromatography–mass spectroscopy (GC–MS) was used to characterize ribbed smoked sheet No. 3 (RSS 3) and latex to confirm the presence of 2,6-dimethoxyphenol odor. The selected ion monitoring (SIM) chromatogram of RSS 3 exhibited a 2,6-dimethoxyphenol peak at m/z 154, while the latex chromatogram did not show any peak. Using a synthetic cis-1,4-polyisoprene rubber and 2,6-dimethoxyphenol mixture as reference, the GC–MS analysis indicated that RSS 3 emits 4.0 mg/kg of 2,6-dimethoxyphenol. When added to the SIM chromatogram, the cobalt (II)-complex of salicylaldehyde ethylenediamine Schiff base ligand (Co(II)-salen) reduced the quantity of 2,6-dimethoxyphenol. This indicates that the odor was oxidized during physical mixing, implying that it was dissolved in the solution. Furthermore, the solubility of 2,6-dimethoxyphenol in various solvents was determined using the Hansen solubility parameters to identify the optimal solvents for the oxidation of the odor by the Co(II)-salen complex. Thermal analysis and theoretical study also indicate the generation of phenoxy radical by Co(II)-salen complex.

Ambient ozone (O₃) is associated with diabetes (DM), but the data on the association between O₃ and fasting blood glucose (FBG) in non-diabetic adults is relatively rare, especially the evidence of urban–rural difference in such association is still lacking. Our research aimed to investigate the potential effects of short-term O₃ exposure on FBG levels. We carried out a cross-sectional analysis of a representative sample of 5329 adults from the Guizhou Population Health Cohort Study and the association between O₃ and FBG levels was determined. In rural populations, subgroup analyses were performed by gender, age, nationality, and so on. We observed that short-term exposure to ambient O₃ was positively associated with FBG levels. The strongest association was observed at lag 015 days, with per 10 μg/m³ increase of O₃ concentration leading to a significant increase of 0.050 mmol/L (95%CIs: 0.037, 0.063) in FBG. More importantly, the association between O₃ and FBG levels was stronger in rural populations with lower income levels. Further stratified analyses showed that participants who were older than 60 years, males, smokers, Han, non-drinkers, warm season, dietary score ≥ 3, exercise time < 150 min/week, sleep duration < 7 h/day, and BMI < 18.5 kg/m² were potentially more susceptible to the effects of O₃ in rural populations. In general, our study not only provided the evidence that O₃ exposure can be associated with increased FBG levels, but also shed new light on the further understanding of the adverse effects of O₃ on DM. Furthermore, rural populations may be more vulnerable to the effects of O₃. Our findings indicate that the issue of air pollution in rural areas is equally noteworthy.

The world ocean fleet consumes around 4.3 million barrels of heavy fuel oil (HFO) daily, releasing large amounts of sulfur-enriched gaseous and particulate pollutants into the atmosphere. The International Maritime Organization (IMO) has set new sulfur content limit values for HFO under the Global Sulfur Cap 2020 (GSC-2020) program to reduce its environmental and public health impact. This study assesses the environmental benefits of the sulfur content limit values for heavy fuel oil set by the IMO on sulfur emissions, trace element concentrations, and ship related PM_2.5 pollution at Paranaguá, the largest grain port in Latin America. X-ray Fluorescence analysis revealed that the concentrations of vanadium (V) and nickel (Ni) in PM_2.5 (i.e., finer particulate matter), which are prevalent trace elements in ship exhaust emissions, decreased significantly from 25.4 ng m^− 3 and 5.8 ng m^− 3 in 2019 to 3.5 ng m^− 3 and 2.2 ng m^− 3 in 2020, respectively. The V/Ni ratio also changed from 4.3 in 2019 to 1.8 in 2020, suggesting significant changes in the signature of marine vessel emission. Sulfur emissions also decreased, with average concentrations of 2.0 µg m^− 3 in 2019 and 1.2 µg m^− 3 in 2020. The primary PM_2.5 concentration, attributed to ship emissions using V as a tracer, was reduced from ~ 80% in 2019 (mean = 35.8%) to less than 5% (mean = 4.9%) in 2020. Inhalation exposure to V and Ni in PM_2.5 showed a decrease in the hazard quotient (HQ) and hazard index (HI) in 2020 compared to 2019, indicating potential health benefits. Our findings underscore the need for more robust international shipping policies prioritizing health objectives and reducing greenhouse gas emissions concurrently. Despite the significant health benefits associated with the implementation of low-sulfur fuels in global shipping, there remains a need for further investigation into the long-term effects of these fuels on air quality and human health.

Green infrastructures have been pointed out as innovative solutions to deal with current and future challenges related to air pollution and climate change. Although the potential of green infrastructures, such as green walls and green roofs, to mitigate air pollution has been documented, evidence at a local scale is still limited. This work aims to increase knowledge about the potentialities of green infrastructures in improving local air quality, focusing on particulate matter, nitrogen dioxide and ozone pollutants, and by using a local-scale computational fluid dynamics model. The ENVI-met model was applied to a particular hour of a summer day over a built-up environment centred on a main avenue in the city of Lisbon (Portugal). The dimensions of the computational domain are 618 m × 594 m × 143 m, and it contains 184 buildings, with the tallest building being 56 m. In addition to the baseline simulation, modelling was also done considering the application of green walls and green roofs to specific buildings located near the main avenue, together with a green corridor. The overall results show no disturbances exerted by green walls on the turbulent flow dynamics and on the air quality levels when compared to the baseline scenario (without green walls). The integrated scenario, which includes green walls, green roofs and a green corridor, will lead to potential local benefits of green infrastructures on O₃ concentrations, followed by variable impacts on NO₂ and particulate matter concentrations.

In air quality modeling, fine-scale daily mapping is generally calculated from dispersion models involving multiple parameters linked in particular to emissions, which require regular updating and a long computation time. The aim of this work is to provide a simpler model, easily adaptable to other regions and capable of estimating nitrogen dioxide concentrations to a good approximation. To this end, we examine the relationship between daily and annual nitrogen dioxide values. We find that this relationship depends on the range of daily values. Then we provide a statistical model capable of estimating daily concentrations over large areas on a fine spatial scale. The model’s performance is compared with standard geostatistical method such as external drift kriging with cross-validation over one year. The reduced computation time means that daily maps can be produced for use by French air quality observatories.

The COVID-19 pandemic has had a significant impact on global health, with millions of people affected by the disease. Recent studies have shown that environmental factors such as air quality, temperature, and humidity can impact the survival and transmission of the virus, leading to differences in the rate of spread and severity of the disease in different regions. In this global cross-sectional study, we analyzed the relationship between environmental factors and the transmission and survival of the virus in 167 cities distributed all over the world. We used a dataset containing daily COVID-19 data for 167 cities from 01/05/2020 to 01/01/2022, along with variables related to atmospheric and environmental conditions. We found an expected positive relationship between increases in atmospheric NO₂ concentration and increases in the infective rate of COVID-19. We also found an unexpected negative relationship between PM10 and COVID-19 spread, which was stronger in unpolluted cities, and indicating a likely stronger and faster deactivation of the viruses by the absorption to the larger than to the smaller particles, to PM10 more than to PM2.5. Although a complete analysis would require taking into account the restrictions in the city and the immunization status of the population, and the variance of COVID-19 spread explained by PM10 was small, only up to approx. 2%, these results contribute to a better understanding of the impact of particles on the spread of COVID-19 and other respiratory viral diseases thus informing public health policies and interventions aimed at mitigating the impact of these pandemics.