One of the biggest causes of worldwide environmental pollution is conventional fossil fuel-based electricity generation. The need for cleaner and more sustainable energy sources to produce power is growing as a result of the quick depletion of fossil fuel supplies and their negative effects on the environment. Solar PV cells employ solar energy, an endless and unrestricted renewable energy source, to generate electricity directly. The optimum output, energy conversion efficiency, productivity, and lifetime of the solar PV cell are all significantly impacted by environmental factors as well as cell operation and maintenance, which have an impact on the cost-effectiveness of power generation. This article presents an analysis of recent research on the impact of operational and environmental factors on the performance of solar PV cells. It has been discovered that temperature and humidity, combined with dust allocation and soiling effect, have a significant impact on the performance of PV modules. In addition, particularly in the lonely places, the wind itself carries a lot of dust and sand particles. The situation gets worse when dust builds up in humid circumstances and produces tenacious, sticky mud on the PV cell, which lowers power output by up to 60–70%. This paper covers cutting-edge methods for reducing these elements’ effects, along with their proportional benefits and difficulties. This paper also explains about the parameters which involved in the solar power production and their influence on the efficiency analysis. The efficiency and energy conversion capacity of the semi conducting materials for power production is also discussed. It is also discussed about the general benefits of the solar PV power generation.

Photocatalysis is playing a vital role for the treatment of recalcitrant pollutants present in water streams. Solar photocatalysis is an environmental friendly and cost-effective technology. Various research works have been reported on the development of novel photocatalysts and the enhancement of their photocatalytic activity. Efforts were being made to apply solar photocatalysis for the removal of pollutants from saline water and industrial saline wastewater. Researchers carried out experimental studies with different reactor configurations such as batch studies, batch recirculation studies, and continuous flow studies with photocatalysts in suspension or photocatalysts in immobilised thin-film systems. The performance of the photocatalytic process was mostly evaluated through percentage removal efficiencies of various parameters such as chemical oxygen demand (COD), biological oxygen demand (BOD) and total organic carbon (TOC). Experimental runs were optimised using Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) techniques. This work is a comprehensive review on the application of solar photocatalysis for the treatment of pollutants present in saline water and industrial saline wastewater. It briefly examines the latest advances in both experimental and theoretical approaches, probing the novel techniques. Finally, this review concludes with current challenges and new directions in this emerging area of research.

Seawater pollution is a significant global environmental problem. Various technologies and methods have been used to remove the contaminants found in saltwater. This experimental study investigates the degradation of contaminants present in seawater using solar photocatalysis, where a combination of TiO2 and ZnO was used. The effects of catalyst dosage, pH, and reaction duration were assessed using percentage removal efficiencies of total organic carbon (TOC), chemical oxygen demand (COD), biological oxygen demand (BOD), and biodegradability (BOD/COD). Biodegradability is essential for removing pollutants from saltwater and plays a vital role. The higher the biodegradability, the more efficient the treatment procedure will be. The most effective percentage reduction rates from the experimental data obtained were TOC=59.80%, COD=75.20%, BOD=23.94%, and biodegradability=0.055. For modeling, optimizing, and assessing the effects of parameters, the Design Expert based on Box Behnken design (RSM-BBD) and a predictive model based on the MATLAB adaptive neuro-fuzzy inference system (ANFIS) tools were used. The coefficient of determination R2 was found to be 0.977 for the RSM-BBD model and 0.99 for the ANFIS model. According to the RSM-BBD design, the maximum percentage pollutant elimination efficiencies were found to be TOC=55.4, COD=73.4, BOD=23.70%, and BOD/COD=0.054, but for the ANFIS model, they were TOC=59.4, COD=75.4, BOD=24.1%, and BOD/COD=0.055. It was discovered that the ANFIS model outperformed RSM-BBD in process optimization. ABSTRAK: : Pencemaran air laut adalah masalah alam sekitar global yang ketara. Pelbagai teknologi dan kaedah telah digunakan bagi menyingkirkan pencemaran yang dijumpai dalam air laut. Kajian eksperimen ini menilai degradasi pencemaran yang hadir dalam air laut menggunakan fotopemangkin, di mana kombinasi TiO2 dan ZnO digunakan. Kesan dos pemangkin, pH, dan tempoh reaksi dipantau menggunakan peratus kecekapan penyingkiran jumlah karbon organik (TOC), keperluan kimia oksigen (COD), keperluan biologi oksigen (BOD), dan kebolehdegradasian (BOD/COD). Kebolehdegradasian adalah sangat penting bagi menyingkirkan bahan cemar dari air laut dan berperanan penting. Semakin tinggi kebolehdegradasian, semakin cekap prosedur rawatan. Peratus kadar pengurangan yang paling berkesan daripada data eksperimen adalah didapati pada TOC=59.80%, COD=75.20%, BOD=23.94%, dan biodegradasi=0.055. Bagi mengkaji kesan parameter terhadap model, kadar optimum, dan memantau keberkesanan parameter, kaedah Pakar Reka Bentuk pada rekaan Kotak Behnken (RSM-BBD) dan model ramalan berdasarkan sistem pengaruh menggunakan sistem MATLAB iaitu Inferens Neural-Fuzi Boleh Suai (ANFIS) digunakan. Pekali penentu R2 terhasil pada 0.977 bagi model RSM-BBD dan 0.99 pada model ANFIS. Berdasarkan reka bentuk RSM-BBD, peratus maksimum keberkesanan penyingkiran bahan cemar dijumpai pada TOC=55.4, COD=73.4, BOD=23.70%, dan BOD/COD=0.054, tetapi bagi model ANFIS, TOC=59.4, COD=75.4, BOD=24.1%, dan BOD/COD=0.055. Model ANFIS adalah lebih berkesan daripada model RSM-BBD dalam proses pengoptimuman.