| 1. |
- Alfeus, Anna, et al.
(författare)
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PM2.5 in Cape Town, South Africa: Chemical characterization and source apportionment using dispersion-normalised positive matrix factorization
- 2024
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Ingår i: Atmospheric Pollution Research. - 1309-1042. ; 15:3
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Tidskriftsartikel (refereegranskat)abstract
- Understanding fine particulate matter (PM2.5) composition and sources is beneficial to improving visibility, addressing climate change, and mitigating poor air quality and related public health effects. Source apportionment techniques have been instrumental in evaluating the impact of sources and secondary processes on the ambient PM2.5 concentrations in receptor areas. Positive Matrix Factorization (PMF) is now the most commonly used tool due to its ability to provide mixture resolution based on available PM2.5 compositional data. Sampling and analysis of PM2.5 was conducted in Cape Town, South Africa from April 2017 to April 2018. The resulting data were dispersion normalized to address the modifications of the source concentrations resulting from the varying dispersion conditions and thereby permit dispersion normalized PMF (DN-PMF) to be employed. DN-PMF quantified the 6 sources as 2-stroke vehicles/galvanizing industries (16.8%); soil/road dust (12.3%); sulphate/marine diesel (3.6%), traffic (15.7%), sea salt (21.8%), and heating/biomass burning/cooking (15.7%). In addition, air mass back trajectory analysis using the Hybrid Single Particle Lagrangian Trajectory (HYSPLIT) model identified long-range transport pathways to Cape Town. The HYSPLIT results showed air masses from the Atlantic SSW (6%), Atlantic SW (24%), Indian Ocean (31%), and Atlantic WSW (39%) influence air quality. The primary sources affected by the transport clusters were heating, 2-stroke vehicles/galvanizing, road and soil dust, and traffic emissions. These results show that reducing emissions from the local sources will improve air quality.
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| 2. |
- Baker, H. Kent, et al.
(författare)
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Payouts and stock ownership
- 2021
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Ingår i: Journal of Multinational Financial Management. - : Elsevier BV. - 1042-444X .- 1873-1309. ; 60
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Tidskriftsartikel (refereegranskat)abstract
- Using a unique Swedish database that records the ultimate stockholdings in public firms, we decompose stock ownership by domiciles using votes rather than cashflows. We then study the impact of variables related to the lifecycle theory of dividends and the catering theory of dividends. We also examine the propensity of firms to pay dividends and/or activate a stock buyback program. Univariate analysis reveals a positive association between a firm’s maturity and its likelihood to pay dividends. Logistic regression finds a positive relation between payouts and retained earnings to total assets. Foreign institutional investors are less likely to hold dividend-paying stocks than domestic institutional investors. The analysis finds no support for the catering theory of dividends. After controlling for stock ownership, our evidence is consistent with the lifecycle theory of dividends, which states that more mature firms are associated with dividends. It also supports the transaction cost hypothesis claiming that foreign investors face additional administrative costs when holding dividend-paying stocks.
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| 3. |
- Fatehi, Hesameddin, et al.
(författare)
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Effect of buoyancy on dispersion of reactive pollutants in urban canyons
- 2022
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Ingår i: Atmospheric Pollution Research. - : Elsevier BV. - 1309-1042. ; 13:8
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we aim to investigate the interplay between chemistry, flow dynamics and temperature using high fidelity computational fluid dynamics (CFD) models in an urban environment. A detailed numerical model based on large eddy simulation (LES) is developed considering the temperature and buoyancy effect and the non-equilibrium chemical processes. The model is used to study flow and reaction inside experimental and real-size street canyons. Street canyons are chosen for this study, as they represent the smallest unit of urban environments where detailed flow simulations combined with chemical reactions can be performed with high numerical accuracy. The effect of thermal driven flow is studied in reacting and non-reacting conditions to understand the role of buoyancy in accurately modeling pollutant reaction and dispersion. It is shown that buoyancy has a significant effect on the dynamics of the flow, by altering the main vortex structure inside the canyon and by increasing turbulent kinetic energy. It is also found that the chemical reactions strongly affect final concentrations of pollutants, which indicates the potential need for implementation of more advanced chemical models in future work. The importance of correct boundary conditions to accurately predict pollutant concentrations are discussed. Finally, by comparing the LES results with experimental field measurements in a real street canyon, the limitation of using periodic boundary conditions, as it is commonly used in the literature, is discussed. Moreover, it is shown that implementation of a variable photolysis rate is likely needed.
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| 4. |
- Kao, Xiaoxuan, et al.
(författare)
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The pressure of coal consumption on China's carbon dioxide emissions: A spatial and temporal perspective
- 2024
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Ingår i: Atmospheric Pollution Research. - : TURKISH NATL COMMITTEE AIR POLLUTION RES & CONTROL-TUNCAP. - 1309-1042. ; 15:8
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Tidskriftsartikel (refereegranskat)abstract
- As the world's largest coal consumer, China is facing the dual challenge of implementing strict coal reduction policies while heavily relying on coal. It is crucial to comprehend the pressures exerted on carbon dioxide emissions from coal consumption as China strives to transition towards a carbon-neutral era. This study defines and classifies the pressure of carbon dioxide emissions resulting from coal consumption, referred to as "carboncoal pressure", in 30 provinces (including municipalities and autonomous regions) from 1997 to 2019. This classification enriches the study of the pressure on carbon dioxide emissions by specific energy types. By calculating the centre of gravity of the carbon-coal pressure and its evolution trends, the spatial pattern of the carbon-coal pressure and the evolution characteristics of the centre of gravity of the pressure are revealed. The results demonstrate that, despite the continuous growth in total coal consumption and carbon dioxide emissions in China, the carbon-coal pressure index exhibits a decreasing trend in certain regions, with significant interregional differences. Most provinces fall into the high-pressure and higher-pressure categories. The number of high-pressure provinces has decreased by 33% from 18 to 8, while the number of low-pressure provinces has risen from 0 to 1, and both higher and medium pressure types have increased. The overall stress index decreases from 0.79 to 0.7. The pressure centre displays a similar spatial trend to the overall changes in coal consumption and carbon dioxide emissions centre, albeit with a smaller magnitude of change. Beijing stands out as the only province with low pressure.
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| 5. |
- Naghibi, Seyed Amir, et al.
(författare)
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Spatiotemporal variability of dust storm source susceptibility during wet and dry periods: The Tigris-Euphrates River Basin
- 2024
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Ingår i: Atmospheric Pollution Research. - 1309-1042. ; 15:1
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Tidskriftsartikel (refereegranskat)abstract
- This study develops a framework for spatiotemporal modeling of dust storm source susceptibility in a critical case study, the Tigris-Euphrates River Basin, as a significant source of dust storms in the Middle East. The study period was divided into four periods, 2000–2004 (hydrological dry year), 2005–2007 (hydrological wet year), 2008–2012 (hydrological dry year), and 2013–2021 (hydrological wet year) representing hydrological conditions in the study area. Initially, visual interpretations of true color composites of the MODIS satellite images were conducted to spot dust storm sources in the studied periods. Topographical, hydrological, soil texture, and vegetation health datasets were prepared to model dust storm source susceptibility in each period. The random forest algorithm was implemented on the four study periods’ datasets. For each period, 70% of dust and non-dust storm sources and conditioning factors were used for training the models. The models were then validated using the validation datasets (remaining 30%), and the importance of the variables was determined for each study period. In the 2008–2012 period, experiencing an extensive drought in the region, a higher number of dust storm sources were detected, and 383 locations (pixels) in the area were considered highly susceptible to dust storm sources. In all study periods, as well as in the ensemble model (integrating the results of four study periods into one overall model), high susceptibility to dust storms was detected in areas where lakes and marshlands had dried up due to climate factors, inappropriate water management strategies, and land use policies. The results also depicted that elevation, wind speed, precipitation, vegetation coverage, slope degree, distance from rivers, and soil texture had high impacts on the susceptibility of land to be a dust storm source.
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| 6. |
- Rahimi, Mostafa, et al.
(författare)
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A novel approach for brake emission estimation based on traffic microsimulation, vehicle system dynamics, and machine learning modeling
- 2023
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Ingår i: Atmospheric Pollution Research. - 1309-1042. ; 14:10
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Tidskriftsartikel (refereegranskat)abstract
- Brake wear is known as the primary source of traffic-related non-exhaust particle generation. Its generation rate is influenced by parameters at different levels: subsystem (type of brakes, pads, materials, etc.), system (vehicles' dynamics, driving style etc.) and suprasystem (road geometries, traffic parameters, etc.). At the subsystem level, we proposed a neural network brake emission modeling, trained and validated through emission data collected from a reduced-scale dynamometer. At the system level, a model of a car dynamics was developed to calculate the wheels’ brake torques and angular velocities. At the suprasystem level, the traffic behavior in a sensitive urban area was characterized experimentally and simulated in a traffic microsimulation software. The vehicle traffic-based records were used to calculate the vehicle dynamic quantities, converted into brake emission through the neural network. To examine the overall traffic impacts on brake emission, the total particle number (PN) and total particle mass were estimated regarding the route choice in the sensitive area and in the whole transportation network. The findings of this study showed significant generation rate of brake emissions (in terms of mass and number) around congested areas (in the order of 10e9 #/s). The brake emission estimation in a real area provides fundamental information to the decision-makers to better insight into the rate of non-exhaust emissions generation.
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| 7. |
- Wang, Yuxuan, et al.
(författare)
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A deep learning approach to real-time CO concentration prediction at signalized intersection
- 2020
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Ingår i: Atmospheric Pollution Research. - : Elsevier BV. - 1309-1042. ; 11:8, s. 1370-1378
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Tidskriftsartikel (refereegranskat)abstract
- Vehicle exhaust emissions at signalized intersections are the essential source of traffic-related pollution to pedestrians. Therefore, it is critical to predicting traffic emissions, especially the hazardous CO gas, with practical and accurate methods. However, the CO emission and concentration at crosswalks can be influenced by the complex traffic conditions in a complicated way, making the prediction of CO concentration a challenging task for traditional statistical models. To this end, a hybrid machine learning framework is proposed in this study to investigate the concentration of CO emissions at pedestrian crosswalks. The proposed method firstly ranks key influencing factors with a random forest approach. Then a prediction model with Multi-Variate Long Short-Term Memory (LSTM) neural networks based on the selected factors is developed. Data is collected at the field intersection for model training and validation. The autoregressive integrated moving average (ARIMA), support vector machines (SVM), radial basis functions network (RBFN), nonlinear vector autoregressive (VAR) and gated recurrent unit ( GRU) neural network are selected as the benchmark models to verify the performance of the proposed model. The Root Mean Square Errors (RMSE), Mean Absolute Error (MAE) and R square are calculated to evaluate the performance of models comprehensively. The results indicated that the proposed model overwhelms the benchmark models in terms of prediction accuracy.
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