Atmospheric Pollution Research最新文献

Vehicle tampering leads to substantial excessive emissions, but few methods could identify the tampered ones from vehicles on road accurately in one day or less. A fast response model based on real time data from terminal box (T-BOX) was built in this study for heavy-duty vehicle tampering identification, which could identify the tampered vehicles from vehicles with excessive emission caused by bad driving conditions, low ambient temperature or on-board diagnostic (OBD) faults. By analyzing the existing means of tampering in the last decade, the vehicle tampering identification model was established according to the data characteristics of tampered vehicles. Two main modules based on emission and emission factors were built and three corrections were added in the model to avoid disturbances led to misjudge. In our research, 66 heavy-duty vehicles from the big data platform were used to screen for vehicle tampering. It was found that 15 vehicles existed excessive emissions, and 2 vehicles were tampered. Tampered vehicles only account for 3% of the sample, but emitted 1.4 times nitrogen oxides (NOx) of total emission of other vehicles. The model solved the problem that the traditional model could not identify the vehicle tampering accurately. It could be used in emission accounting and management of tampered vehicles for government.

This study compares dust storm simulations using two commonly adopted methods for representing four important dust emission parameters. Compared with a dynamic dust source mask based on land use and vegetation cover, a static mask based solely on land use overestimates dust concentration and optical depth by a factor of 2, besides generating spurious emissions. The results reinforce that seasonal variations in vegetation cover can significantly affect dust emissions. For sandblasting efficiency, a clay-dependent semiempirical expression produces 12 times more dust than does a physics-based expression. Simulations using model-predicted versus constant air density differ by only 8%. However, this difference (often overlooked) could range between 12% and 22% for annual simulations over global dust source regions. Simulations with updated versus old land use data, using the same dust source mask, differ twofold, indicating the significant impact of land use change on regional dust emission in central Arizona. The difference between simulations within each of the four pairs is generally larger than the uncertainty due to meteorology. The simulations align better with observation when using the dynamic dust source mask, the physics-based sandblasting efficiency, and the up-to-date land use data. Given the high sensitivity of dust to surface conditions, the results discussed have implications for improving the dust cycle in weather and climate models and for interpreting model intercomparisons.

Light-duty vehicular exhaust remains one of the key sources of ambient air pollution globally, despite concerted mitigation efforts by the countries worldwide. It is of top scientific interest to explore vehicular variables affecting such emission from passenger cars through real-time monitoring (N = 1561). The research investigated emission parameters such as CO, HC, CO₂, O₂, λ (Lambda) and Air-fuel ratio (AFR), alongside the vehicular variables, namely, age, mileage, emission norm and maintenance category. The model-oriented study found the car age (R_rangeA = 0.81–0.98 for E_COI; 0.72–0.96 for E_HCI; 0.74–0.91 for λ_FI and 0.75–0.93 for AFR_FI, respectively) and mileage (R_rangeM = 0.71–0.98 for E_COI; 0.75–0.95 for E_HCI; 0.69–0.93 for λ_FI and 0.68–0.92 for AFR_FI, respectively) to be the most significant aspects. Further, the study reported that the emissions improved with the progression of in-use norms (tighter the norm, lower the emission). Interestingly, the maintenance level of cars is found to be directly and inversely related to both CO and HC emissions in different testing modes. It further presents car model-wise emission equations for car age and mileage as which can be used to accurately predict the exhaust emission from cars. The research outlines the need to incorporate car mileage, maintenance level and applicable emission norm into the present environmental policy, particularly in the developing countries. An improved emission testing, real-time emission data and appropriate environment regulation are the three major steps towards urban air quality improvement policy.

Atmospheric air pollution exposure raises morbidity and mortality rates and is a major cause of the world's illness burden. In this context, we explored spatial and temporal trends in particulate matter PM10 from 2003 to 2020 over Morocco to assess air pollution exposure. We use the capabilities of ML models to study PM10 trends using 26 predictor variables, including meteorological parameters, volatile organic compounds, atmospheric oxidants, and aerosol optical depth data from the Copernicus Atmosphere Monitoring Service (CAMS). For this purpose, three ML models were built: Multiple Linear Regression (MLR), Random Forest Regression (RFR), and Generalized Additive Model (GAM). To match and optimize these models, a set of ML algorithms has been coupled with each model. The results show all these models are highly accurate in predicting and forecasting PM10 total column trends. Cross-validation showed that GAM had better prediction ability for the PM10 total column with R² = 0.994 and a very low root mean squared error (RMSE) not exceeding 0.046 × 10⁻¹⁶ kg/m². The GAM model showed much higher predictive ability and lower bias than the other models. This finding can be explained by the advantages of GAMs, including their ability to capture complex and non-linear patterns in the data, making them particularly useful when relationships are not easily represented by linear models. This study has presented a comprehensive methodology for predicting the spatiotemporal variability of PM10. The proposed methodology holds potential applicability across all regions, facilitating the generation of high-resolution PM10 monitoring and the establishment of systems for the early detection of air pollution incidents in Morocco. Furthermore, the developed models exhibit versatility, enabling their application for estimating future trends of individual pollutants or making real-time predictions of air quality levels. This research contributes to advancing the understanding and proactive management of air quality in the context of Morocco, offering valuable insights for pollution control efforts.

Nitrogen dioxide (NO₂) is an air pollutant highly impacting on human health, and its measurement is crucial for air quality assessment. Use of passive samplers for long-term large-scale monitoring is a reasonably reliable and economic alternative to more sophisticated and expensive equipment employed in active air sampling by environmental control authorities. In recent years the Citizen Science approach, based on low-cost devices, is spreading more and more in environmental control. Passive samplers available on the market (like the consolidated “Palmes” tubes) are often used in community-based monitoring campaigns. We describe validation of a new cheap axial diffusion tube for NO₂ monitoring, used in combination with a new user-friendly App for smartphone that represents an innovation to speed up recording of geo-localization and exposition period data. Affordability and availability of materials, simple construction protocol and easy App procedure, allow possible self-production by school students and non-expert users, making the proposed tube a potential tool to realize extended Citizen Science monitoring campaigns. Accuracy within 25% and precision within 20%, evidenced in validation, show comparability of the tube performance with Palmes-type tubes and agreement with the official monitoring station results. Two small-scale trial monitoring campaigns, involving high school students, were performed to test the efficacy of the proposed “tube-App” system in combining educational impact and community value of air quality monitoring.

The granulometric fractions of indoor dust, categorized as coarse (grain size of 1.00–0.071 mm) and fine (grain size <0.071 mm), were investigated to discern variations in their magnetic properties and contents of potentially toxic heavy metals. Monthly dust samples were gathered from January 2021 to December 2022 from a private apartment situated on the outskirts of a large urban agglomeration (Warsaw, Poland). To assess indoor dust, several magnetic parameters, including mass-specific magnetic susceptibility, were employed. Portable X-ray fluorescence measurements were utilized to evaluate the enrichment of granulometric fractions in harmful heavy metals. The study reveals a comparable composition of magnetic minerals irrespective of grain size (magnetite and metallic iron), with variations observed in the domain state of magnetic particles (contribution of single-domain (SD) grains to multi-domain (MD)). Seasonal fluctuations were predominantly noted in the distribution of the fine fraction's mass during the warm season (May–July). A notable increase was observed in the fine fraction's mass contribution to the total dust mass compared to the winter season (December and February). The fine fraction was highly enriched in toxic metals, including Pb, Cr, Cu, Mn, Fe, and Sr. Pollution Load index is 6–8 for the fine fraction and 2–8 for the coarse fraction. The increase in the fine fraction mass induces linear changes in magnetic susceptibility, likely associated with the rise in anthropogenic magnetic particles. This finding holds significant implications for human health, as fine particles laden with toxic heavy metals can enter the human respiratory tract causing adverse health effects.

Despite significant emissions of fine particulate matter (FPM) from the Indo-Gangetic Plain (IGP) that affect the climate and air quality in the region, the sources of these emissions are not adequately addressed. This research uses a combined radiocarbon-molecular organic tracer technique to investigate the degree of contamination, seasonal fluctuations, and contribution of FPM in the middle IGP (Patna), India. The findings indicated levoglucosan (L) as the single primary BB tracer chemical, ranging from 149 ng/m³ to 490 ng/m³ (median 282 ng/m³). Winter (median 462 ng/m³) showed a 2–3 times higher level of L than the monsoon season (median 180 ng/m³). A significant association of L with other organic tracers such as galactosan (G), mannosan (M), vallinic acid, syringic acid, p-hydroxybenzoic acid (pHA) and dehydroabietic acid (DHAA)(r = 0.53 to 0.89, p < 0.05), and moderate connection with Cl⁻ (r = 0.21, p < 0.05), SO₄²⁻ (r = 0.29, p < 0.05), and NO₃- (r = 0.22, p < 0.05) indicated significant BB contribution. However, non-sea salt (nss-K⁺) was not related to L. Based on seven days of air mass back trajectories and MODIS active fire counts analysis, we conclude that OAs composition is not the local origin but is also impacted by long-range atmospheric transport from Pakistan/Afghanistan, followed by the Bay of Bengal and the Arabian Sea. Chemical analysis of organic tracers and positive matrix factorization (PMF) modeling study identified three unique sources, i.e., biomass burning, secondary aerosols formation, and mixed type (fossil fuels and construction dust) as the primary source of FPM in Patna, accounted for 46.1 %, 28.9 %, and 24.9 %, of total emissions, respectively. The radiocarbon (¹⁴ C) analysis of total carbon (TC) samples further supported this conclusion. The results of ^{the 14} C study indicated that emissions from BB, such as wood and stubble, were responsible for 57% of the TC concentration.

In compression ignition engines, the use of hydrogen-diesel dual fuel mode has a positive impact on engine performance and emissions. To enhance the impact of hydrogen in dual-fuel mode, it is crucial to properly adjust the energy ratio and design the combustion chamber for dual-fuel mode. This study focuses on these two situations. The study conducted a literature review and designed and manufactured two combustion chambers (Natural Gyration 1, Natural Gyration 2) suitable for dual fuel mode. Using the original combustion chamber and the manufactured combustion chambers, at a constant engine speed of 1850 rpm, at five different loads (3, 4.5, 6, 7.5, and 9 Nm), and at three different hydrogen injection times (1.6, 1.8, and 2.0), tests were performed. Engine performance and emission data obtained as a result of the tests were examined. Tests revealed that at a load of 9 Nm and with a hydrogen energy ratio of 12%, the Natural Gyration 1 combustion chamber increased the internal cylinder maximum pressure by 1.41%, reduced the specific energy consumption by 2.29%, and reduced particulate emissions by 8.82%. On the other hand, it was determined that the Natural Gyration 2 combustion chamber reduced the maximum cylinder internal pressure by 1.98%, increased the specific energy consumption by 2.66%, and soot emissions by 5% at the same load and hydrogen energy ratio.

The indoor air of nail salons is full of volatile organic compounds (VOCs), many of which have fragrances. Little is known about the fragrance chemicals in nail salons, as fragrance ingredients are not required on nail product labels and are considered trade secrets. This study aimed to identify fragrance chemicals and their potential sources and exposures in nail salons. Indoor air samples were collected in seven nail salons in the Greater Boston Area between November 2016 and June 2017. Personal samples were also collected from ten nail salon workers during their work shifts. Follow-up area sampling was performed in two salons one year after the initial visits. All air samples were collected using thermal desorption (TD) tubes and analyzed on a TD-gas chromatography/mass spectrometry (GC/MS) system targeting 55 fragrance chemicals. Eighteen compounds were detected in air samples, including terpenes, alcohols, carbonyls, ethers, and esters. The concentrations displayed limited spatial variation within a salon but moderate variation over time. The highest median personal inhalation concentrations were benzaldehyde (36.4 μg/m³), 2-ethylhexanol (30.0 μg/m³), d-limonene (16.6 μg/m³), and 2-butoxyethanol (12.6 μg/m³). Highest personal levels were reached by maximum concentrations of 2-butoxyethanol (<1611 μg/m³)), d-limonene (<413 μg/m³), and methyl salicylate (<113.5 μg/m³). Personal concentrations of most compounds were highly correlated with area concentrations (Spearman correlations = 0.69−0.92). Fragrance concentrations from area and personal air samples did not correlate significantly with the ventilation rate. Cleaning agents, personal care products, and nail products were identified as important possible emission sources. This study reveals a subset of fragrance chemicals in nail salons’ indoor air and calls for future research on a full spectrum of these chemicals, their health effects among nail salon workers, and ways to reduce these exposures.

Traffic-related air pollutants are predominantly emitted in urban environments; therefore, analyzing their impact on indoor air quality (IAQ) is important. Effectively managing IAQ is vital, given the extensive duration individuals, particularly students, spend indoors. This study conducted a quantitative assessment of black carbon (BC) and indoor and outdoor particulate matter (PM_2.5, particles with diameter ≤2.5 μm) concentrations in five South Korean classrooms to determine the root cause and effects of traffic-related air pollutants on indoor environments. The research specifically focused on indoor BC levels during rush hours, that is, periods marked by increased traffic volume. The analysis revealed that the mean indoor and outdoor BC concentrations in the five classrooms were measured at 1.03 (95% CI: 0.69, 1.36) and 1.38 (95% CI: 0.79, 1.96) μg/m³, respectively, while the mean PM_2.5 concentrations were measured at 17.07 (95% CI: 12.92, 13.62) and 29.89 (95% CI: 10.88, 48.89) μg/m³, respectively. The indoor-to-outdoor (I/O) ratio of BC in the five classrooms during the occupied period was 0.77 (95% CI: 0.69, 0.84) and 0.62 (95% CI: 0.51, 0.72) for PM_2.5. During the unoccupied period, the I/O ratio of BC was 0.68 (95% CI: 0.63, 0.72) and 0.53 (95% CI: 0.42, 0.63) for PM_2.5. The rise in urban traffic increased the BC outdoor level by 47% and the PM_2.5 concentration by 13%. Classrooms situated closer to roadways had higher BC levels than those located at a greater distance. During rush hours, the BC concentration in the classroom closest to the major road was 2.03 (95% CI: 1.78, 2.27) μg/m³, while the furthest classroom recorded a concentration of 0.88 (95% CI: 0.79, 0.96) μg/m³. During commuting times, classroom BC concentrations increased by up to 5.86 μg/m³ owing to student door-opening activities, increasing the I/O ratio by approximately 20%. Consequently, the average BC concentration in classrooms during rush hours was approximately double that recorded during non-rush hours (0.73 μg/m³). These findings are instrumental in developing strategies to enhance IAQ in educational settings and guiding urban planning decisions regarding the location of schools.