Atmospheric Pollution Research最新文献

Multi-step forecasting of air pollutants extends the horizon for individuals and authorities to take informed actions for mitigating potential risks. Due to the instability of air pollutants, current research primarily focuses on relatively short-term forecasting, with achieving ultra multi-step forecasting presenting a significant challenge. In response to this issue, this study proposes a novel model: Frequency Enhanced Decomposed Temporal Convolution Networks (Fed-TCN) to achieve ultra multi-step forecasting. This study applies time-frequency transformation to explore the frequency characteristics of air pollutants and extract long-term patterns. These patterns are then fed into TCN to enhance the accuracy of ultra multi-step forecasting. Extensive experiments were conducted on eight air pollutants at four monitoring stations in Shanghai. The results indicate variations in forecastable ranges for different pollutants. NO and NO_x can be forecasted up to one week, while NO₂, CO, SO₂, and O₃ require forecasting within 1–3 days (approximately 24–72 steps ahead). Furthermore, PM_2.5 and PM₁₀ can only be forecasted for short-term periods, not exceeding 12 h. When compared to baseline models, Fed-TCN achieves a 4.3%–11% lower Mean Absolute Error. Moreover, Fed-TCN provides insights into the contribution of pollutant composition patterns to forecasting accuracy. In general, daily patterns, semi-commuting patterns, and residuals contribute 68.8%–81.7% to ultra multi-step forecasting. The proposed method is applicable for ultra multi-step forecasts of other regions and different types of air pollutants.

Air sampling was done inside adult and children's hospitals that were selected to treat severe cases of COVID-19. Influence of peripheral factors such as particle concentration, air velocity, sampling point dimensions, distance from the patient bed, sampling time, the flow rate of sampling pump, and factors related to COVID-19 patients (disease period, mask use, number, and age) were analyzed using multivariate analysis (RT-PCR). The results showed that 5.8% (N = 8) of indoor air samples were positive for the presence of the coronavirus. The presence of viruses in the indoor air of hospitals has a strong positive relationship with particles and the age of patients while it has a reverse relationship with the air cleaner, ventilation system, and distance from the patients. Therefore, the higher particle concentration, the age of hospitalized patients, and the remarkable number of patients increase the probability of the presence and identification of the coronavirus in the indoor air of hospital wards. Also, the presence of an air cleaner, a suitable ventilation system especially a mechanical one, and increasing the distance from the patients reduces the possibility of virus existence in the indoor air and its identification. In general, the results showed that the adult hospital has more polluted indoor air than the children's hospital in terms of the presence of SARS-COV-2. Sanitation and engineering measures like upgrading the ventilation system, particularly in vulnerable wards of hospitals are recommended.

The occurrence, source, and health risk of polycyclic aromatic hydrocarbons (PAHs), nitro-PAHs (NPAHs), and oxy-PAHs (OPAHs) in indoor dust collected from three types of microenvironments (offices, classrooms, and dormitories) at a university campus in Zhengzhou were analysed. The results showed that the average concentration of the total PAHs was 2403.21 ± 981.99 ng g⁻¹, with PHE (20.06% of Σ18PAHs) and BkF (15.46%) being the dominant species. The average concentrations of total NPAHs and OPAHs were 11.09 ± 5.12 and 385.80 ± 255.27 ng g⁻¹, with 2+3N-FLT (39.15%) and 9-FLO (39.76%) as the most abundant species of NPAHs and OPAHs, respectively. The average concentrations of total PAHs, NPAHs, and OPAHs in different microenvironments displayed obvious spatial specificity with the highest values in dormitories. The main source of PAHs, as identified by diagnostic ratios, was petroleum combustion, coal/biomass combustion and vehicle exhaust emissions. The average incremental lifetime cancer risk (ILCR) for undergraduates exposed to dust in classrooms and dormitories exceeded 10⁻⁶, indicating a potential cancer risk. The most important exposure pathway was dermal contact, while the inhalation route was negligible.

There is ongoing concern about trace element (TE) emissions to the global environment from the dusts generated by open pit mining of coal, iron ore, stone quarries, and aggregate extraction. However, the chemical composition and acid solubility of these dusts is highly variable. Here, TEs were determined in snow collected in 2016 and 2017 in the vicinity of open-pit bitumen mines in northern Alberta, Canada. Acid solubility was assessed quantitatively by comparing TE concentrations in leachates and acid digests. The mineralogical composition of the particles extracted from the snow was examined using SEM-EDS. The data is reproducible from one year to the next. TE concentrations were greater throughout the industrial zone compared to the reference location (UTK), with the midpoint between the two central upgraders being especially impacted. Regardless of their geochemical class (lithophile: Al, Be, Cs, La, Li, Sr, Th; chalcophile: As, Cd, Pb, Sb, Tl; or enriched in bitumen: Mo, Ni, V), all TEs showed strong, positive correlations with Y, a conservative element which serves as a surrogate for the abundance of mineral particles. The ratio V:Ni in the snow is less than the corresponding values for bitumen and petcoke, but similar to that of local road dust. The ratio La:Al in snow is elevated, relative to the earth's crust, suggesting an enrichment of heavy minerals monazite and zircon. The predominance of quartz and other stable silicates helps to explain the limited chemical solubility of the dusts, and predicts a low bioaccessibility of these TEs in the environment.

In this study, microplastics were monitored separately in the wet and dry deposition in the peri-urban area of Thailand. Over eighteen weeks from September 2021 to January 2022, rainwater (wet deposition) and settled microplastics (dry deposition) were systematically collected. Subsequent analyses involved the extraction, quantification, and identification of microplastics through wet peroxide oxidation, density separation, stereomicroscopic visual inspection and Fourier-transform infrared (FTIR) analysis. The results show that the wet deposition flux of microplastics was averaged at 285 numbers/m²/day while the dry deposition flux of microplastics was averaged at 199 numbers/m²/day, contributing to a total deposition flux (dry + wet) of 325 numbers/m²/day. Comparative assessments with prior studies from different locations demonstrated similarities in microplastic numbers, color and polymer types. Notably, this study found a higher microplastic deposition flux during the period when wet deposition existed than the flux during the period with only dry deposition, underscoring the important role of rainfall in adding more removal of microplastics from the atmosphere. These findings provide crucial insights into the dynamics of microplastic deposition in outdoor settings, significantly contributing to understanding the microplastic pollution cycle in the environment.

In this study, we explore the influence of the baghouse differential pressure, the baghouse running time, and activated carbon addition on dioxin emissions at the baghouse outlet during waste incineration. The experimental results showed that there was a critical value of the baghouse differential pressure. When the baghouse differential pressure was less than the critical value, the dioxin toxic equivalent at the baghouse outlet decreased with an increase in the differential pressure; and when the baghouse differential pressure was greater than the critical value, the dioxin toxic equivalent increased with an increase in the differential pressure. There was also a critical value for the influence of the baghouse running time on the dioxin emissions at the baghouse outlet. Within the range of experimental data, as the baghouse running time increased, the dioxin toxic equivalent at the baghouse outlet first decreased and then maintained a stable fluctuation. The addition of activated carbon effectively reduced the dioxin toxic equivalent at the baghouse outlet, which decreased with an increase in the amount of added activated carbon. This study provides a preliminary reference and data support to control dioxins during waste incineration.

Analyzing the spatial correlation of synergistic effect of reducing air pollutants and carbon dioxide (SERAC) between cities, which are spatial hubs characterized by dense populations, industries, and energy usage, provides a foundation for devising regional joint prevention and control policies tailored to local circumstances at this scale. The study analyzed 43 cities in the Bohai Rim region, assessing synergistic emission reduction effects from 2006 to 2020 using a coupled coordination model based on air pollutants (AP) and carbon dioxide (CO₂) emission trends. Social network analysis is employed to investigate the structural characteristics and robustness of the spatial correlation network and to unveil the position and significance of each city within the network. The study found that: (1) AP emission equivalent peaked in 2011, while the growth rate of CO₂ emission equivalent slowed down in the Bohai Rim region. (2) The low-value areas for the SERAC are spatially clustered in Beijing, Tianjin, and Tangshan, with 90% of the cities in a coordinated state. (3) The network showcasing the spatial correlation between the SERAC features both “local clustering” and “global correlation” coexisting, and its robustness needs improvement. (4) Membership within the plates is relatively stable. On this basis, it is proposed that the Bohai Rim region can enhance the SERAC by establishing a “lead-follow” inter-city mechanism, strengthen the stabilizing role of core entities, implement differentiated emission reduction measures in communities, and expand regional cooperation, ultimately reducing costs and increasing efficiency.

While ducted dust emissions from industry are usually well regulated and controlled, fugitive dust emissions can be difficult to quantify or differentiate from off-site emissions, particularly where fugitive emissions are transient or intermittent. In this study, sources of nuisance dust were investigated using a novel dust deposition sampling methodology, followed by chemical and mineralogical analysis using optical microscopy, x-ray diffraction (XRD), scanning electron microscopy with energy dispersive x-ray spectrometry (SEM-EDS), and isotope ratio mass spectrometry analysis. Dust deposition over 24 h was collected on horizontally orientated 300 mm square glass panels at 6 sites in the region of interest. This method captured very small (<10 mg) masses of dust and could distinguish very small variations in dust deposition at different sites and on different days. Comparison samples collected from potential dust sources at a nearby lime manufacturing facility, and other potential fugitive dust sources in the region were differentiated using XRD mineralogy, visual appearance by optical microscopy and SEM-EDS analysis of individual particles. Comparison of selected dust samples to reference samples indicated most dust deposition at the study sites consisted of soil or sand. However, dust deposition at two sites was sometimes composed of a material that was similar in composition to lime that had been rehydrated to portlandite, but not yet fully carbonated to calcite. Subsequent analysis of δ¹³C and δ¹⁸O for selected dust samples from these two sites were consistent with rapid and recent carbonation of small particles in an alkaline environment, which could have occurred during the atmospheric release and transport of lime, and ruled out fugitive dust emission from lime stockpiles existing on site. Combining the information obtained from high frequency record of dust deposition with targeted mineralogical, isotopic and morphological examinations provided new insight into the source of fugitive dust emissions in this area.

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.