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In this study, a batch adsorption process was conducted utilizing Ascophyllum Nodosum biosorbent as an organic adsorbent used for the elimination of heavy metal ions (specifically chromium, nickel, and zinc) from wastewater. To examine the surface morphology and functional groups, the biosorbent underwent analysis using FTIR, SEM & EDX technologies. To understand the nature of the adsorption process, several isothermal studies were conducted. These studies aimed to determine whether the adsorption process was homogeneous or heterogeneous in nature. Furthermore, the physical or chemical nature of the metal ion adsorption on the adsorbent was investigated through kinetic studies. Thermodynamic investigations were also carried out to assess whether the adsorption of metal ions was exothermic or endothermic, as well as to conclude the spontaneity of the process. Notably, the findings indicated that the inclusion of 0.3N HCl resulted in the highest rate of desorption, indicating its effectiveness in releasing the adsorbed metal ions. The AN – Brown Algae adsorbs targeted metal ions (Cr(VI), Ni(II), Zn(II)) at percentage of 93.55%, 87.56% and 83.27% respectively from the synthetic solution under the optimum conditions in batch study.

In this study, neem leaf biosorbent, prepared through chemical synthesis and activated with concentrated sulfuric acid, was utilized as an organic adsorbent for the removal of heavy metal ions, specifically arsenic (As) and chromium (Cr), from wastewater. The characteristics of the biosorbent were thoroughly analyzed using BET, FTIR, SEM, and EDX analysis to evaluate the surface morphology and functional groups. The experimental results indicated that under optimal conditions, with a pH of 10.0, a biosorbent dose of 3 g/L, an initial concentration of 20 mg/L, and a contact time of 60 minutes, 89.74% of As(II) and 82.32% of Cr(VI) metal ions were efficiently removed from synthetic solutions. To understand the nature of the adsorption process, various isothermal studies were conducted to determine whether it was homogeneous or heterogeneous. In addition to the above-mentioned experiments, kinetic studies were conducted to assess the physical or chemical nature of the metal ion adsorption on the adsorbent. Thermodynamic investigations were also performed to determine whether the adsorption of metal ions was exothermic or endothermic and whether the process was spontaneous. The results from the desorption studies revealed that the addition of 0.3N HCl resulted in the highest rate of desorption. This effectively facilitated the recovery of the metal ions from the spent adsorbent, indicating the effectiveness of 0.3N HCl in desorbing the adsorbed metal ions.

Air pollution monitoring is becoming increasingly important, with an emphasis on the effects on human health. Because nitrogen dioxide (NO2) and sulphur dioxide (SO2) are the principal pollutants, many models for forecasting their potential harm have been created. Nonetheless, making precise predictions is nearly impossible. The prediction of air pollution enables researchers to understand how pollution affects human health. Deteriorating air quality can lead to respiratory diseases such as lung cancer and asthma. The effect of pollution on environmental degradation can also be predicted and reductions can be detected in the ozone layer. This study also focuses on and promotes the development of smart city environments by obtaining influential pollutants that affect the air, thereby reducing the source of specific pollutants An Artificial Neural Network (ANN) model is used as a forecast the pollution and the starling murmuration optimization (SMO) procedure is used to optimise the Artificial Neural Network strictures to achieve a lower forecasting error. Furthermore, in this research work, we used real time dataset as we have used Winsen ZPHS01B sensor module to collect the data, which is stored in cloud platform. After the composed data is used to train and test, after this process we will evaluate the results. To assess the performance of the suggested model. Furthermore, the perfect has been tested using two alternative kinds of input parameters: type as, which contains various lagged values of variables (NO2 and SO2), and type as, which only includes lagged values of the yield variables. The collected findings suggest that the projected model is more precise than existing joint forecasting benchmark models when different network input variables are considered.

Every year, the number of people who die because of pollution rises, with air pollution being the major cause. Air pollution is generated by a variety of factors, one of which is pollution caused by automobiles. Technological improvements make great efforts to detect accidents and monitor the increasing pollution. In this paper, a novel Internet of Things-based Vehicle Gas Emission Monitoring System (IoT-VGEMS) to monitor and control the reduction of gas emissions. The overall work is categorized into two sections: One is Transmission Part, and the other is receiving part. The Transmitting Part consists of Arduino UNO, MQ-135 Sensor, and nRF24L01 transmitter. The Receiving Part consists of Arduino UNO, LCD Display, GSM, and nRF24L01 receiver. Initially, creating code to sense the data from the MQ-135 sensor to the Arduino UNO and store the data in Arduino 1 is fetched to transfer with the help of the nRF24L01 module. Using an Arduino UNO to interface with a GSM module. GSM module that would deliver the warning and intimation messages to the vehicle owner and display them on LCD. Finally, GSM can able to send a message to the respective user if the pollution level exceeds the threshold. Hence the message from our device will give information about the emission and the user could rectify it. Few people will not take any action even if they receive a warning from transport officials, they will be penalized further.