Atmospheric Pollution Research最新文献

The aim of this study is to estimate the contribution of the maritime sector on the air quality of major Mediterranean ports with the Long Term Ozone Simulation-European Operational Smog (LOTOS-EUROS) chemical transport model, using two source apportionment methods: the brute-force emission scenario methodology and the labelling approach. With the brute-force method NO₂ shipping concentrations are estimated by the ratio of the difference between two model runs - with original and reduced emissions - and the equivalent emission reduction factor. In the labelling, emitted species are explicitly labelled per sector and tracked through all model processes. Simulations are performed for a one-year period, from May 2018 to May 2019 and the two methodologies are intercompared. The methods show strong agreement between NO₂ shipping contributions (R > 0.95) and differences of ∼14%. A sensitivity analysis carried out using the brute-force method indicates that a linear regime between NO_X emissions and NO₂ concentrations holds, when adopting a low to moderate emission reduction (<50%). We applied the brute-force method with an assumed 20% emission reduction and found that the NO₂ surface concentrations attributed to maritime sector activities in selected ports were between 5% and 70% of the total NO₂ surface concentrations, with a mean value of 27%. Comparisons between measurements from the European Environment Agency (EEA) ground-based monitoring stations and LOTOS-EUROS NO₂ surface concentrations show a strong correlation (R∼0.8) with an underestimation of the model (∼-32%) for the whole period. The bias is reduced to −20% when air quality monitoring stations affected by traffic and industrial activities are excluded from the analysis. Moreover, observed Sentinel-5 Precursor TroPospheric MOnitoring Instrument (S5P/TROPOMI) and modelled NO₂ vertical column densities (VCDs) show a significant spatial agreement (R∼0.86) for both summer and winter with biases of −25% and −1%, respectively, over the selected ports. These comparisons were carried out as an indirect way of validating the applied methodology and the performance of the model in coastal areas. The present study provides a solid background which will enable the assimilation of the satellite observations to the CTM to infer NO_X shipping emissions in the Mediterranean Basin in view of the upcoming designation of the Mediterranean Sea as an Emission Control Area in 2025.

The service offered by commercial kitchens and the catering industry is becoming more and more popular all around the world. However, the health risks of sector workers are often overlooked. In this study, size-segregated Particulate Matter (PM) samples were collected from the indoor environments of five commercial kitchens, and elemental concentrations were quantified to estimate the corresponding health risks. Lung-deposited fractions of trace elements in three different regions of the respiratory tract were estimated and used to assess carcinogenic and non-carcinogenic health risks. Besides, the deterioration in lung capacity of the cooks was also evaluated by spirometry tests to correlate indoor pollutants. Exposure to trace elements was dominated by Na, S, Al, Fe, and Mg. Estimated carcinogenic risk through inhalation exposure to Cr was between the moderate risk level of 10⁻⁶ to 10⁻⁴, while the Hazard Index (HI) values were lower than the acceptable level of 1. This, along with elevated indoor PM levels, represented a significant potential for carcinogenic health hazards for the kitchen staff. According to the results of the lung function tests, a significant decrease was determined in the respiratory functions of the cooks, and it was associated with elevated exposure to fine PM, Pb, Cr, Cu, K, Ni, and Mn. Workplace health and safety measures to better protect the health of kitchen staff are urgently needed and should attract more public attention.

Exposure to particulate matter (PM) has been associated with several adverse health outcomes. Studies indicate that children may be exposed to much higher concentrations of PM at school than in other environments. There exists very little data on the deposited dose of PM while children attend classes. This study was carried out in a school located near an industrial complex in Portugal and attended by children aged 3–12 years. Indoor PM₁₀, PM_2.5 and PM₁ were measured over two seasons in classrooms representing different school year groups. Particle deposition fractions in the respiratory tract, as well as the deposited doses, were calculated using the Multiple-Path Particle Dosimetry (MPPD) and the Exposure Dose Model (ExDoM2). Both models were implemented assuming an 8-h exposure scenario to represent the school day. In general, differences in PM concentrations were observed depending on room occupancy periods and season. The highest mean PM_2.5 concentration was recorded in winter when the classroom was vacant (23.7 ± 20.5 μg m⁻³), while the highest mean PM₁₀ level was observed in spring during school hours (61.7 ± 24.2 μg m⁻³). Regardless of the dosimetry model, the highest deposition of PM₁₀ and PM_2.5 was in the upper region, while the lowest was in the tracheobronchial (TB) region. The results indicate that deposited dose and deposition fraction in spring may be more harmful to pupils’ health than in winter. PM₁₀ presented the highest doses, ranging from 54.2 to 128 μg and from 83.9 to 185 μg, according to MPPD and ExDoM2 estimates, respectively.

Motorized vehicle operation stands as a significant contributor to urban air pollution, necessitating immediate attention to prevent its adverse impacts on human mortality and morbidity. However, estimating traffic emissions at a local scale in developing countries is challenging due to heterogeneous traffic conditions. This study proposes an integrated modelling framework called VISSIM-MOVES Integration Model for Delhi City (VMIMD), designated to assess emissions from the congested road network in Delhi's central business district. In addition, the study revises the default emission inventory of the MOVES model by applying correction factors (CFs) developed during the study. These CFs are tailored to address heterogeneous traffic conditions typical of developing nations like India. The application of the CFs has significantly enhanced the overall efficiency of the model for Indian conditions. The study's findings indicate a significant correlation between the duration vehicles spend in different operating conditions (idle, cruise, and braking) and the emission generation. Notably, during heavy traffic hours, emission increases significantly as vehicles reduce their speed and require higher power to transition between different operating modes. The study also includes a comparison between the emissions estimated by VMIMD and emission standards. The results indicate significant differences in emissions, especially during peak traffic hours.

California has a long history of wildfires that release substantial amounts of smoke, contributing to regional air pollution. This study employs advancements in Earth observation satellites, cloud computing, and the Google Earth Engine to analyze MODIS MCD19A2 Version 6 level 2, Sentinel-5P NRTI AER AI, and Sentinel-5P NRTI NO2, evaluating the impact of wildfires from 2010 to 2022 on air quality in California. Historical fire events are cross-validated using the MODIS 1-km MYD14A1 V6 dataset, and the MODIS MAIAC-derived Aerosol Optical Depth (AOD) values are validated against AERONET AOD measurements from ground-based sun photometers. To assess the analysis's uncertainty, metrics such as Mean Absolute Error, Relative Mean Bias, and Root Mean Square Error are applied. Additionally, linear regression is used to verify the correlation between satellite and ground measurements. Results show that the average monthly MODIS MAIAC AOD at 470 nm and 550 nm tends to overestimate AOD by 24% and 6%, respectively, compared to AERONET AOD values. The correlation coefficient and adjusted R-squared value range from 0.78 to 0.80 and from 0.61 to 0.65, respectively, for AOD values at 470 nm and 550 nm. During the fire season, correlations exhibit closer associations, with a coefficient above 0.78 and an adjusted R-squared value above 0.63 are observed for both wavelengths. Sentinel-5P data reveal a significant increase in NO₂ concentration in the surrounding areas during the 2020 and 2021 wildfires. This study identifies historical spatiotemporal trends of air pollution attributable to wildfires in California from 2010 to 2022, informing decision-making in the development of effective prevention and mitigation programs.

Prior research indicates an association between fine particulate matter (PM_2.5) and blood lipids, but fewer studies have explored the constituents of PM_2.5. This study investigated the long-term effects of PM_2.5 and its constituents on blood lipid levels and dyslipidemia. We used generalized linear mixed models to evaluate the associations of 3-year mean concentrations of PM_2.5 and its constituents (BC, NH₄⁺, NO₃⁻, SO₄²⁻, OM) with blood lipids and dyslipidemia, followed by stratified, interactive, and sensitive analysis. Out of 29,223 participants, there were positive associations between increased PM_2.5 and its constituents with total cholesterol (TC), low-density lipoprotein cholesterol (LDL–C), while a negative association with high-density lipoprotein cholesterol (HDL–C). Notably, BC and OM exhibited the most significant association with HDL–C and LDL–C, each interquartile range (IQR) increment of BC and OM were positively associated with higher LDL–C (Percentage change: 21.17%, 95% CI:19.58%, 22.77%; 23.05%, 95% CI: 21.53%, 24.58%), and lower HDL–C (19.23%, 95% CI: 18.27%, 20.17%; 18.72%, 95% CI: 17.81%, 19.62%), respectively. PM_2.5 and its constituents were found to be associated with an elevated risk of dyslipidemia. BC, SO₄²⁻, and OM were particularly associated with hypoalphalipoproteinemia and hyperbetalipoproteinemia. Stratified analyses showed that females, individuals residing in rural areas, older adults, those who were overweight or obese, non-smokers, non-drinkers, and regular exercisers displayed higher susceptibility to PM_2.5 and its constituents. These findings have significant implications for reducing the harm of air pollution to dyslipidemia.

Various attempts using different platforms to understand the chemical composition of the Asian Tropopause Aerosol Layer (ATAL) have concluded that it primarily consists of nitrate (NO₃⁻) aerosols. Recent in-situ measurements from aircraft flying through ATAL have suggested that ammonia (NH₃) pollution from Asia could serve as the precursor for nitrate aerosols in ATAL through gas-to-particle formation after undergoing convective uplift to the Upper Troposphere and Lower Stratosphere (UTLS) regions. In this study, we aimed to investigate the potential pathways of convective transport of lower-atmospheric pollutants by analysing the chemical composition of rainwater and aerosols in a highly convective region, Kolkata (Head Bay of Bengal), India. We utilized the PILS-IC system established at Kolkata Camp Observatory of the National Atmospheric Research Laboratory (KCON). The analysis revealed a significant dominance of NH₄⁺ and NO₃⁻ ions in both rainwater and aerosol samples during the monsoon and post-monsoon seasons, with the dominance increasing during the post-monsoon season. Air mass trajectories indicated a clear influence of the Indo-Gangetic Plain (IGP) region during the post-monsoon season, which is well-known for its dense agricultural activities and industries. High concentrations of NH₃ even at higher altitudes, have been observed in Atmospheric Infrared Sounder (AIRS) measurements. Moreover, using Outgoing Longwave Radiation (OLR) and vertical wind as proxies for tropical deep convection and updrafts/downdrafts, respectively, we found evidence supporting the possibility of convective transport of these lower-atmospheric pollutants to the UTLS regions during the monsoon season. In conclusion, this study provides evidence for the potential convective transport of lower-atmospheric pollutants, including NH₃ and other precursor gases, to higher levels. These pollutants could partially serve as the source of nitrate aerosols in the ATAL through gas-to-particle formation processes.

This study investigates the spatiotemporal variability of surface ozone (O₃) over the Arabian Peninsula (AP) between 2005 and 2019, focusing on the Arabian Gulf (AG). The analysis explores the relationship between surface O₃ data from the Copernicus Atmosphere Monitoring Service (CAMS) with boundary layer height (BLH), 2 m temperature (T2M), downward ultraviolet radiation at the surface (UVB), and 10 m wind speed (WS) and direction from the fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis (ERA5). Also, the study considers Carbon Monoxide (CO) and Nitrogen Oxides (NO_x) surface emissions from the Tropospheric Chemical Reanalysis version 2 (TCR-2). Furthermore, it investigates the impact of El Niño Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) on surface O₃ variations on a seasonal scale. Surface O₃ observations from 15 ground-based stations across the AP were used to evaluate CAMS-O₃, showing a good agreement between CAMS and the observations. The analysis of mean diurnal variations of CAMS-O₃ and ERA5 reveals that surface O₃ is highest over the eastern parts of the AP, mainly the AG, peaking during summer, followed by spring, fall, and winter. This seasonal cycle is also observed, to a large degree, in BLH, T2M, UVB, and WS. The results also reveal insignificant correlation between surface O₃ and ENSO, but stronger correlation with IOD, especially over the AG during summer and fall. The analysis indicates that elevated T2M and UVB during daytime and elevated BLH during nighttime are significant contributors to increased levels of O₃ over the AG.

Promoting the transport industry to achieve a carbon peak and controlling carbon emissions in various regions of China through structural adjustment are important means to significantly reduce CO₂ emissions. To explore the drivers of carbon emissions from China's transportation sector from 2006 to 2021. And constructs the IPAT-S (IPAT model considering structural adjustment) model, the structural equation is combined to explore the carbon emission reduction path of China's transportation industry from the perspective of structural adjustment. The results show that the structural adjustment is beneficial to alleviate the pressure of emission reduction, carbon emissions from the national transportation sector show a pattern of “low in the north and high in the south”, with the trend gradually shifting to the central region, and the spatial structural effect on carbon emissions has been significantly strengthened, the industrial structure, energy structure and transportation structure still contribute to the carbon emissions of the transportation industry. From the regional level, there are some differences in the impact of structural adjustment on carbon emission reduction in different regions, but optimizing the energy structure will be conducive to carbon emission reduction. In the case of the Northeast region, the energy consumption structure, industrial structure, and transportation structure do not inhibit carbon emissions, and the structural emission reduction in the Central region has begun to bear fruit. The structural adjustment in the eastern and western regions has yet to be improved, which will also provide the government with policy recommendations for structural emission reduction based on regional differentiation.

Poor air quality can provoke severe impacts on health, necessitating environmental monitoring of atmospheric particulate matter (PM) to assess potential threats to human well-being. However, traditional continuous air quality monitoring systems are often costly and time-consuming in data treatment. Lately, there is a growing trend towards the use of low-cost wireless PM sensors, providing more detailed information than standard systems. This paper presents a system designed to measure air quality, specifically, a wireless sensor network composed of a distributed sensor network linked to a cloud system. The proposed system can efficiently measure air quality as it is cost-effective, small-sized, and consumes little power. Sensor nodes based on low-power long range (LoRa) motes transmit field measurement data to the cloud via a gateway, and a cloud computing system is implemented to store, monitor, process, and visualise the data. Advanced techniques were included in our cloud for data processing and analysis to optimise the detection of PM. Laboratory and field tests in the historic Riotinto mine validate the system's viability, offering real-time air quality information for nearby populations. Once calibrated, sensors demonstrate high accuracy, presenting mean error of −0.3% and low deviation (R² = 0.96) when compared to regulatory systems for both low (<10 μgPM₁₀/m³) and hazardous concentrations (300 μgPM₁₀/m³), which makes them perfect as early warning systems for atmospheric pollution in mining.