A 3D numerical model of the municipal solid waste incineration (MSWI) process was constructed based on a grate furnace with a daily processing capacity of 800 tons. Fluent was used for analyzing key factors affecting the concentration and diffusion level of particulate matter (PM). According to the actual MSWI plant working condition, a 3D model of the incinerator and the waste heat boiler has been constructed under benchmarks. Key factors affecting PM generation were determined by combining mechanistic knowledge and experts’ experience. They were the combustion temperature of solid phase municipal solid waste (MSW), the wall’s PM collision mode, and the second baffle length. Subsequently, the process of resolving the 3D numerical model was delineated. Then, a univariate analysis of the aforementioned 3D model was conducted for the three pivotal factors mentioned above. Conclusively, the effect of the important factors on the number of particles at the outflow of the incinerator was analyzed via orthogonal experiments to obtain the optimal combination. PM concentration initially diminished and then rose with the increased combustion temperature of the solid-phase MSW. Furthermore, a noteworthy reduction in PM concentration was observed when the second baffle length was 12.45–12.95 m. The greatest influence on the PM concentration of the outlet was posed by the wall’s PM collision mode, followed by the second baffle length. The appropriate adjustment of the combustion temperature of the solid-phase MSW, selection of wall materials, and design of the second baffle length were beneficial for diminishing PM concentration and ensuring long-term stable operation of the MSWI process. The combinative optimality of the three key factors was acquired via orthogonal experiments, which proved the subsequent optimal control of PM concentration at the outlet.

Deterioration of water quality of lakes and reservoirs has become major global concerns that impose serious environmental impacts for both aquatic and terrestrial environments. In the current study, many parameters like temperature (Temp), electric conductivity (EC), dissolved oxygen (DO), turbidity (TU), pH, biological oxygen demand (BOD), chemical oxygen demand (COD), total alkalinity (TA), total dissolved solids (TDS), total organic carbon (TOC), nitrate(NO₃⁻), phosphate (PO₄³⁻) and chlorophyll a (chl-a) were determined. The study covered the Angereb reservoir and its tributaries on a monthly basis from January to March 2019 at five sampling stations in accordance with APHA 2017 guide lines for physicochemical analysis. The values of all the investigated parameters, except DO (at AU, AD, KU and KD), COD and TU, were below the maximum permissible limits set by WHO. Thus, the findings for DO, TU and COD demonstrated that remedial actions should be taken to improve the quality of the water in the reservoir and its tributaries. Multivariate statistical methods (PCA and CA) were applied to detect spatial and temporal variations of water quality parameter. The first three principal components were enough to develop the PCA score plot which explained about 71.32% of the total variance in the dataset. The PCA and CA have provided similar information; grouped the 24 samples into 3 significant clusters showing spatial variations but minimal temporal variations were observed within the samples collected in the period of January in the reservoir site. The water quality parameters, TU and BOD, were moderately positively loaded on the space of the first principal component and were found to be associated with each other, whereas the EC and TDS have shown moderate negative loading and positively associated with each other. This study suggested PCA and CA methods found to be useful tools for monitoring and controlling water quality parameters for selected sampling stations of surface water.