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The school buildings in and around the city of Patras in the prefecture of Achaia, Greece, are classified on the basis of the prevailing environmental conditions and the existing human activities in the surrounding area. School buildings in urban and rural environment are investigated. The type and the sources of pollutants reaching the soil or the air in the school environment, air particles and noise are considered in this investigation. A number of school buildings have been constructed near traffic roads. As a result of this, the school environment is affected by noise and air particles produced from the combustion of petrol by the vehicles. In other cases, fine particles, related to resuspension of soil of the school yard and the surrounding area, affect the school environmental conditions. Similarly, under specific meteorological conditions, some schools in the rural environment can be affected by the agriculture activities, such as the use of fungicides. Specific measures are suggested to be taken for all groups of schools for the protection of the students' and the teachers' health.

Recently, the environmental pollution is becoming a challenging issue due to the lacking of awareness about the importance of the environment and the growth of transportation facility and the various factories. The Internet of Things (IoT) technology is useful today for collecting and updating the data dynamically from anywhere anytime in less expense. Air quality of different cities is predicted by considering and maintaining the above listed technique which is useful for identifying and predicting the air quality and measure the air pollution level as well. This paper proposes a new IoT aware air quality prediction system that incorporates the newly proposed classifier with fuzzy temporal correlation and the auto-encoded Bidirectional Long Short-Term Memory (Bi-LSTM) to predict the diseases such as heart, cancer and diabetes. Moreover, we propose a new optimization algorithm called Spotted Hyena and Personalized best Cuckoo Search Optimization Algorithm (SHPCSA) for selecting and optimizing the most contributed features that are helpful for enhancing the prediction accuracy with short span of time. The air quality data is collected from various cities like Chennai, Bong Kong, etc. to conduct experiments. The proposed model is proved better than standard classifier with respect to precision, recall, F1-score and accuracy by conducting experiments

Wastewater from textile industry is considered one of the major environmental challenges due to the large volume of highly colored, polluted and toxic effluent. This study investigated the treatability of real textile wastewater by pilot-scale anoxic-aerobic Membrane Bioreactor (MBR) system without sludge wasting for operation period of 100 days. The proposed system was investigated under different Internal Recycle (IR) ratios and the impact of IR ratio on Total Organic Carbon (TOC), Total Nitrogen (TN) and Color removals were examined. Under IR ratios between anoxic and aerobic tanks of 0.0, 0.5 and 2.0, the respective average removal efficiency of TN was 20.9%,53.4% and 71.7%, whereas average color removal of 81%, 85% and 88%, respectively was noted. The results indicated that increase of recycle ratio from 0.5 to 2.0 enhanced TN removal to about 71% and color removal to above 85%. The IR between anoxic and aerobic tanks has a significant role in TN and color removal due its effect on the development of bacterial communities. On the other hand, the results indicate over 93% TOC removal, which was independent of IR ratio.

Transportation driven by fossil fuels increases air pollution induced by urbanisation. Heavy metals such as Cadmium (Cd), Chromium (Cr), Copper (Cu), Iron (Fe), Lead (Pb), and Zinc (Zn) are life-threatening elements and their concentration is increasing alarmingly in soil in various countries. Air pollution and associated metals contaminate the environment and affect nature's abiotic and biotic components. The significance of tree species originates from their connection to the effects of pollution and the solution approach to their biological cycle. Nineteen distinct tree species dominate the region, with heavy metals sinking in the range of Cd<0.4, Cr<1, Cu<15, Fe<2, Pb<5, and Zn<2 mg/kg or parts per million. According to the findings of this study, tree species can sink heavy metals. The different trees have distinct metals-sink ratios. The competence results showed the order of heavy metal sink as Delonix Regia-red (4) > Delonix Regia-yellow (4) > Syzygium Cumini (3) > Muntingic Calabura (3) of the trees with the highest sink rates and nine species had absorbed lead metal. The nine tree species were unsuitable for heavy metal absorption or deposition. There, Albizia Lebbeck (T02), Biancaca Decapetela (T05), Brucea Javanica (T06), Ficus Elastica (T09), Ficus Religiosa (T10), Pithecellobium Dulce (T13), Tamarind (T17), Terminalia Catappa (T18), and Thespesia Populnea (T19) were very low, less than 10% of other dominant tree species. Syzygium cumini (T16) is a high-yielding tree species that absorb significant quantities of Lead (Pb) and Copper (Cu). These studies confirm that, tree species can capture several heavy metals simultaneously from the polluted environment.