The COVID-19 pandemic: prediction study based on machine learning models

Environmental Science and Pollution Research - COVID-19 was first discovered in Wuhan, China in December 2019. It is one of the worst pandemics in human history. Recent studies reported that...     

Development of DPSIRO framework indicators for municipal solid waste management: a case of Bahir Dar city,Ethiopia

Journal of Material Cycles and Waste Management - The generation of municipal solid waste (MSW) in urban areas is rising globally and is having a negative impact on social, economic, and...     

Modeling Flow,Nutrient, and Sediment Delivery from a Large International Watershed Using a Field‐Scale SWAT Model

A large international watershed, the St. Clair‐Detroit River System, containing both extensive urban and agricultural areas, was modeled using the Soil and Water Assessment Tool (SWAT) model. The watershed, located in southeastern Michigan, United States, and southwestern Ontario, Canada, encompasses the St. Clair, Clinton, Detroit (DT), Sydenham (SY), Upper, and Lower Thames subwatersheds. The SWAT input data and model resolution (i.e., hydrologic response units, HRUs), were established to mimic farm boundaries, the first time this has been done for a watershed of this size. The model was calibrated (2007–2015) and validated (2001–2006) with a mix of manual and automatic methods at six locations for flow and water quality at various time scales. The model was evaluated using Nash–Sutcliffe efficiency and percent bias and was used to explore major water quality issues. We showed the importance of allowing key parameters to vary among subwatersheds to improve goodness of fit, and the resulting parameters were consistent with subwatershed characteristics. Agricultural sources in the Thames and SY subwatersheds and point sources from DT subwatershed were major contributors of phosphorus. Spatial distribution of phosphorus yields at HRU and subbasin levels identified locations for potential management targeting for both point and nonpoint sources and revealed that in some subwatersheds nonpoint sources are dominated by urban sources.     

Enhanced struvite recovery from wastewater using a novel cone-inserted fluidized bed reactor

The feasibility of struvite recovery at low （12.5 mg/L） and high （120 mg[L） phosphorus concentrations was studied by constructing a novel fluidized bed reactor with cones （FBRwc） and without cones （FBRwoc）. The crystallization process was continuously operated for 133 days under different hydraulic retention times （HRT = 1-10 hr）, pH （7.5-10）, and molar ratios of Mg/P （0.75-1.75）, N/P （1-10） and Ca/Mg （0-2）. The optimum operating conditions of HRT, pH, Mg/P and N/P molar ratios were found to be 2 hr, 9, 1.25, and 7.5, respectively. Under these optimum conditions, the phosphorus precipitation efficiencies of FBRwc were 93% for low and 98% for high phosphorus influent; however, the efficiencies were 78% and 81% for FBRwoc, respectively. Due to crystal losses at each junction （17%-31%）, the crystal recovery efficiency of FBRwoc was relatively low （47%-65%） for both influent concentrations. However, the losses were minimal in FBRwc, which showed 75% and 92% crystal recovery for low and high phosphorus concentrations, respectively. At low calcium concentration, crystal chemical analysis showed the product to be pure struvite （〉 99%）. The scanning electron microscope and X-ray diffraction results further confirmed that the crystal recovered from FBRwc contained pure struvite, which could be considered a high quality fertilizer. Except HRT, all parameters （pH, Mg/P, N/P and Ca/Mg） were found to be influencing factors for FBRwc performance. Overall, inserting cones in each part of the reactor played a significant role in enhancing struvite recovery from a wide range of phosphorus-containing wastewater.     

Roadside vehicle particulate matter concentration estimation using artificial neural network model in Addis Ababa, Ethiopia

Currently, vehicle-related particulate matter is the main determinant air pollution in the urban environment. This study was designed to investigate the level of fine (PM_2.5) and coarse particle (PM₁₀) concentration of roadside vehicles in Addis Ababa, the capital city of Ethiopia using artificial neural network model. To train, test and validate the model, the traffic volume, weather data and particulate matter concentrations were collected from 15 different sites in the city. The experimental results showed that the city average 24-hr PM_2.5 concentration is 13%–144% and 58%–241% higher than air quality index (AQI) and world health organization (WHO) standards, respectively. The PM₁₀ results also exceeded the AQI (54%–65%) and WHO (8%–395%) standards. The model runs using the Levenberg-Marquardt (Trainlm) and the Scaled Conjugate Gradient (Trainscg) and comparison were performed, to identify the minimum fractional error between the observed and the predicted value. The two models were determined using the correlation coefficient and other statistical parameters. The Trainscg model, the average concentration of PM_2.5 and PM₁₀ exhaust emission correlation coefficient were predicted to be (R² = 0.775) and (R² = 0.92), respectively. The Trainlm model has also well predicted the exhaust emission of PM_2.5 (R² = 0.943) and PM₁₀ (R² = 0.959). The overall results showed that a better correlation coefficient obtained in the Trainlm model, could be considered as optional methods to predict transport-related particulate matter concentration emission using traffic volume and weather data for Ethiopia cities and other countries that have similar geographical and development settings.