Environmental science: atmospheres最新文献

Organic aerosols (OA) constitute an important fraction of fine particulate matter (PM_2.5) air pollution, yet accurate and efficient OA modeling within chemical transport models (CTM) remains a challenge. Volatility basis set (VBS) schemes for OA have demonstrated improved performance in simulating OA, particularly for primary organic aerosol (POA), but their computational complexity impedes application to advanced modeling tasks, such as detailed source apportionment. Conversely, simpler “two-product” schemes are efficient and compatible with source apportionment techniques but many of them tend to overestimate POA by treating it as non-volatile. Either VBS or 2-product schemes can perform well for secondary organic aerosol (SOA) depending upon the data and assumptions used to model SOA formation from precursors. In this study, we update the Comprehensive Air Quality Model with extensions (CAMx) “SOAP” 2-product modeling framework by (1) treating POA as semivolatile using an efficient scheme, (2) adding SOA formation from semivolatile organic compounds (SVOCs), and (3) adopting SOA yields derived from the widely-used Community Multiscale Air Quality (CMAQ) AERO7 scheme. The first update allows temperature-dependent partial evaporation of POA to SVOC, which is subsequently oxidized in the gas phase. For the latter two updates, SOA yields are updated to emulate the AERO7 scheme based on an offline conceptual model. We implemented these changes within the existing SOAP2 scheme of CAMx to create a new scheme called “SOAP3”. A series of CTM simulations were conducted with the SOAP3 scheme to simulate OA and its components in China during July and November 2018. Results were validated against surface observations and compared to the SOAP2 and AERO7 schemes. Compared to SOAP2, SOAP3 substantially reduced POA proportions (by 10–24%) and increased SOA concentrations (by 45–193%) for selected regions. SOAP3 performs more like the AERO7 scheme than SOAP2 in terms of the simulated OA components and improved accuracy compared to observations. Uncertainties and limitations of the current SOAP3 scheme are also discussed. Our study demonstrates a feasible and readily implemented methodology for improving two-product OA modeling, which is currently employed in many CTMs.

Harmful algal blooms (HABs) are projected to become increasingly prevalent, extending over longer periods and wider geographic regions due to the warming surface ocean water and other environmental factors, including but not limited to nutrient concentrations and runoff for marine and freshwater environments. Incidents of respiratory distress linked to the inhalation of marine aerosols containing HAB toxins have been documented, though the risk is typically associated with the original toxins. However, aerosolized toxins in micrometer and submicrometer particles are vulnerable to atmospheric processing. This processing can potentially degrade HAB toxins and produce byproducts with varying potencies compared to the parent toxins. The inhalation of aerosolized HAB toxins, especially in conjunction with co-morbid factors such as exposure to air pollutants from increased commercial activities in ports, may represent a significant exposure pathway for a considerable portion of the global population. Understanding the chemistry behind the transformation of these toxins can enhance public protection by improving the existing HAB alert systems.

An unmanned aerial vehicle was deployed daily in Riverside, CA from August to November 2020, capturing vertical ozone and particulate matter measurements. Flights took place in the early morning and late afternoon, resulting in 321 vertical profiles from the surface to 500 m above ground level. The measured ozone mixing ratio is statistically compared with ground-based measurements at the Riverside–Rubidoux regulatory air monitoring site in Jurupa Valley, CA and with CMAQ simulated concentrations to assess consistency with the nearest reference monitor and model skill at reproducing the observed vertical structure, respectively. The default model configuration overestimates ground-level ozone by 17.7 ppb in the morning and underestimates it by an average of 2.9 ppb in the afternoon. The sensitivity of the model to factors such as planetary boundary layer (PBL), eddy diffusivity, NO_x emissions, and VOC emissions is investigated by modifying key physics and emissions settings in a series of simulations. We found that our default PBL scheme used in the default CMAQ simulation negatively biases the PBL height in the nighttime and positively biases it in the daytime compared to the observations retrieved from a ceilometer. For the observational region of interest, NO_x emissions are concluded to be largely underestimated, leading to biases in modeled ozone concentration. We conclude with recommendations for achieving model parity with localized measurements.

Due to rapid urbanization and lifestyle changes, people in developing countries like India spend most of their time indoors, just like those in developed countries. Indoor air pollution (IAP) studies in urban built environments in India are yet to gain momentum. Studies conducted so far are restricted to reporting pollutant concentration, providing limited insights into pollutants' source, transport, and fate. Comprehensive studies are critical to assessing IAP severity and developing and deploying effective mitigation strategies in built environments. The present study includes spatio-temporal monitoring of particulate matter (PM) in a multizonal residential apartment using a network of low-cost air quality monitors and research-grade instruments to characterize emission sources, assess transport metrics, estimate spatial exposure, calculate I/O ratios, and assess efficacies of different mitigation measures. Sub-micron particles dominated number size distribution for cooking and incense. Operation of air conditioners (AC) led to faster transport of pollutants from the kitchen to the bedrooms. PM exposure in all zones relative to the kitchen had comparable (∼0.8–0.9) exposure during cooking. The average I/O ratios during cooking were elevated throughout the apartment, with the kitchen (10.1 ± 8.9) and bedrooms (7.2 ± 5.7 & 7.4 ± 5.9) being the highest and lowest, respectively. Natural ventilation through balcony doors led to an average exposure reduction of 74–86% in different zones. AC operation reduced cumulative exposure, which was further reduced upon affixing a filter sheet on the AC pre-filter. Among the mitigation measures assessed, the highest cumulative loss rate (2.3 ± 0.1 h⁻¹) was observed for the portable air cleaner with the default HEPA filter.

The planetary boundary layer (PBL) plays a crucial role in determining meteorological fields and the diffusion of atmospheric pollutants. Therefore, accurate PBL simulation is necessary for precise meteorological and air quality simulations, and the choice of PBL scheme significantly influences the accuracy of simulation results. In this study, we investigate the seasonal and diurnal variations of typical meteorological variables over the Yangtze River Delta (YRD) region by using the Weather Research and Forecasting (WRF) model using four different closure schemes. These closure schemes include two non-local closure schemes, i.e., Yonsei University (YSU) and Asymmetric Convective Model version 2 (ACM2), as well as two local closure schemes named Mellor–Yamada–Janjic (MYJ) and Mellor–Yamada Nakanishi and Niino (MYNN). By comparing observations and model inter-comparisons, we discuss the similarities and differences in simulated results among different PBL schemes. The results indicate that local closure schemes, i.e., MYJ and MYNN, generally produce more realistic simulations of meteorological parameters. MYNN performs best in summer with a mean bias (MB) of 0.41 °C for temperature and 0.44 m s⁻¹ for wind speed, while MYJ shows better results under stable conditions during winter with a MB of 0.64 °C for temperature and −5.76% for relative humidity. YSU is found to have less bias in PBL height during summer with the highest R up to 0.81, while MYJ outperforms the three other schemes with the least MB of 38 m (R = 0.65) in winter. Each PBL closure scheme, i.e., the MYJ and MYNN local closure schemes, may not accurately capture all physical processes, leading to performance variations, especially during transitional seasons and under specific diurnal conditions. Thus, it is important to note that each scheme has its strengths and weaknesses, and the selection of the most appropriate scheme should depend on the specific variables and scenarios under consideration.

This study estimates PM_2.5 concentrations in Ouagadougou using satellite-based aerosol optical depth (AOD) and meteorological parameters such as temperature, precipitation, relative humidity, wind speed, and wind direction. First, Simple Linear Regression (SLR), Multiple Linear Regression (MLR), Decision Tree (DT), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) models were developed using the available labeled data (AOD and meteorological parameters with corresponding PM_2.5 values) in the city. The XGBoost model outperformed all other models that were used, with a coefficient of determination (R²) of 0.87 and a root-mean-square error (RMSE) of 15.8 μg m⁻³ after a five-fold cross-validation. The performance of the supervised XGBoost model was upgraded by incorporating a semi-supervised algorithm to use large amounts of unlabeled data in the city and allow for a more accurate and extensive estimation of PM_2.5 for the period 2000–2022. This semi-supervised XGBoost model had an R² of 0.97 and an RMSE of 8.3 μg m⁻³ after a five-fold cross-validation. The results indicate that the estimated 24 hour mean PM_2.5 concentrations in the city are 2 to 4 times higher than the World Health Organization (WHO) 24 hour guidelines of 15 μg m⁻³ in the rainy season and 2 to 22 times higher than the WHO 24 hour guideline in the dry season. The results also reveal that the average annual estimated PM_2.5 concentrations are 11 to 14 times higher than the WHO average annual guideline of 5 μg m⁻³. Finally, we find higher PM_2.5 concentrations in the city's center and industrial areas than in the other areas. The results indicate a need for future air pollution policy and mitigation in Burkina Faso to achieve desired health benefits such as reduced respiratory and cardiovascular problems, which will, in turn, lead to decreased PM_2.5 mortality rates.

The present long-term study has been conducted with dual objectives: firstly, to monitor the spatio-temporal variation of ambient air quality parameters and secondly, to evaluate the impact of air pollutants on the Delhi population. Five years (January 2019 to December 2023) of data of six key pollutants (PM₁₀, PM_2.5, NO₂, O₃, Benzene, and Toluene) were collected by continuous ambient air quality monitoring stations, obtained from the Central Pollution Control Board portal. The impact of air pollutants on human health was assessed using different indices and the AirQ+ model developed by the World Health Organization (WHO). Additionally, the ozone formation potential (OFP) of benzene and toluene was evaluated. The findings of the study revealed that the concentrations of PM₁₀ and PM_2.5 exceeded both national and global guidelines across all the sites throughout the study period. Notably, industrial sites were classified as the severe category according to the National Air Quality Index. At industrial sites, the OFP of volatile organic compounds (VOCs) was observed to be higher in comparison to commercial sites. The AirQ+ model analysis in the health risk assessment indicated a strong association between PM₁₀ exposure and mortality from respiratory (91.36%) and chronic bronchitis (90.85%) diseases. Additionally, long-term PM_2.5 exposure was linked to an increased risk of stroke (65%) and circulatory (63.83%) mortality.

Climate warming is projected to be particularly pronounced in the northern high latitudes coupled with reduced light availability due to increased cloudiness. The changing climate may alter the fluxes of greenhouse gases (GHGs) and atmospherically reactive trace gases, which can drive important climate feedbacks. We investigated the individual and combined effects of warming and increased cloudiness on methane (CH₄), carbon dioxide (CO₂), nitrous oxide (N₂O), nitric oxide (NO), nitrous acid (HONO) and biogenic volatile organic compound (BVOC) fluxes in mesocosms from two tundra and one palsa mire ecosystems kept under strict environmental control in climate chambers. We also examined whether and how prevailing soil physiochemical properties and plant species composition affected the fluxes. In control conditions, all sites were net sinks of CH₄ and CO₂ during both growing seasons except for the palsa site which was a net source of CO₂ in the second growing season. Warming enhanced CH₄ uptake, mostly observed in the palsa site, and turned the palsa site from a sink to a source of CO₂ in the first growing season and increased the CO₂ source strength in the second growing season. Warming increased BVOC emissions while increased cloudiness mostly decreased the emissions. The combined treatment of warming and increased cloudiness decreased CH₄ uptake, mostly observed in the palsa site, and BVOC emissions. Fluxes of CO₂ were linked to availability of soil carbon and organic matter, litter input, soil pH and bulk density, and cover of mosses. Low emissions of N₂O, NO, and HONO could mainly be explained by limited availability of mineral nitrogen. Warming-enhanced CH₄ uptake and BVOC emissions will provide a negative feedback to climate while enhanced CO₂ release from palsa mires will exacerbate global warming. Under combined warming and increased cloudiness, subarctic ecosystems may shift from sinks to sources of CH₄, providing a positive feedback to climate. Prevailing soil physiochemical properties and vegetation composition will play a significant role in controlling the fluxes, hence contributing to the overall climate change effects and feedback.

In the last few decades, atmospheric formation of secondary organic aerosols (SOA) has gained increasing attention due to their impact on air quality and climate. However, methods to predict their abundance are mainly empirical and may fail under real atmospheric conditions. In this work, a close-to-mechanistic approach allowing SOA quantification is presented, with a focus on a chain-like chemical reaction called “autoxidation”. A novel framework is employed to (a) describe the gas-phase chemistry, (b) predict the products' molecular structures and (c) explore the contribution of autoxidation chemistry on SOA formation under various conditions. As a proof of concept, the method is applied to benzene, an important anthropogenic SOA precursor. Our results suggest autoxidation to explain up to 100% of the benzene-SOA formed under low-NO_x laboratory conditions. Under atmospheric-like day-time conditions, the calculated benzene-aerosol mass continuously forms, as expected based on prior work. Additionally, a prompt increase, driven by the NO₃ radical, is predicted by the model at dawn. This increase has not yet been explored experimentally and stresses the potential for atmospheric SOA formation via secondary oxidation of benzene by O₃ and NO₃.