Background: The environment has been significantly impacted by rapid urbanization, leading to a need for changes in climate change and pollution indicators. The 4IR offers a potential solution to efficiently manage these impacts. Smart city ecosystems can provide well-designed, sustainable, and safe cities that enable holistic climate change and global warming solutions through various community-centred initiatives. These include smart planning techniques, smart environment monitoring, and smart governance. An air quality intelligence platform, which operates as a complete measurement site for monitoring and governing air quality, has shown promising results in providing actionable insights. This article aims to highlight the potential of machine learning models in predicting air quality, providing data-driven strategic and sustainable solutions for smart cities. Methods: This study proposed an end-to-end air quality predictive model for smart city applications, utilizing four machine learning techniques and two deep learning techniques. These include Ada Boost, SVR, RF, KNN, MLP regressor and LSTM. The study was conducted in four different urban cities in Selangor, Malaysia, including Petaling Jaya, Banting, Klang, and Shah Alam. The model considered the air quality data of various pollution markers such as PM_2.5, PM₁₀, O₃, and CO. Additionally, meteorological data including wind speed and wind direction were also considered, and their interactions with the pollutant markers were quantified. The study aimed to determine the correlation variance of the dependent variable in predicting air pollution and proposed a feature optimization process to reduce dimensionality and remove irrelevant features to enhance the prediction of PM_2.5, improving the existing LSTM model. The study estimates the concentration of pollutants in the air based on training and highlights the contribution of feature optimization in air quality predictions through feature dimension reductions. Results: In this section, the results of predicting the concentration of pollutants (PM_2.5, PM₁₀, O₃, and CO) in the air are presented in R² and RMSE. In predicting the PM₁₀ and PM_2.5concentration, LSTM performed the best overall high R²values in the four study areas with the R² values of 0.998, 0.995, 0.918, and 0.993 in Banting, Petaling, Klang and Shah Alam stations, respectively. The study indicated that among the studied pollution markers, PM_2.5,PM₁₀, NO₂, wind speed and humidity are the most important elements to monitor. By reducing the number of features used in the model the proposed feature optimization process can make the model more interpretable and provide insights into the most critical factor affecting air quality. Findings from this study can aid policymakers in understanding the underlying causes of air pollution and develop more effective smart strategies for reducing pollution levels.

Environmental issues such as environmental pollutions and climate change are the impacts of globalization and become debatable issues among academics and industry key players. One of the environmental issues which is air pollution has been catching attention among industrialists, researchers, and communities around the world. However, it has always neglected until the impacts on human health become worse, and at times, irreversible. Human exposure to air pollutant such as particulate matters, sulfur dioxide, ozone and carbon monoxide contributed to adverse health hazards which result in respiratory diseases, cardiorespiratory diseases, cancers, and worst, can lead to death. This has led to a spike increase of hospitalization and emergency department visits especially at areas with worse pollution cases that seriously impacting human life and health. To address this alarming issue, a predictive model of air pollution is crucial in assessing the impacts of health due to air pollution. It is also critical in predicting the air quality index when assessing the risk contributed by air pollutant exposure. Hence, this systemic review explores the existing studies on anticipating air quality impact to human health using the advancement of Artificial Intelligence (AI). From the extensive review, we highlighted research gaps in this field that are worth to inquire. Our study proposes to develop an AI-based integrated environmental and health impact assessment system using federated learning. This is specifically aims to identify the association of health impact and pollution based on socio-economic activities and predict the Air Quality Index (AQI) for impact assessment. The output of the system will be utilized for hospitals and healthcare services management and planning. The proposed solution is expected to accommodate the needs of the critical and prioritization of sensitive group of publics during pollution seasons. Our finding will bring positive impacts to the society in terms of improved healthcare services quality, environmental and health sustainability. The findings are beneficial to local authorities either in healthcare or environmental monitoring institutions especially in the developing countries.

In this study, a polar extract of Aconitum lycoctonum L. was used for the synthesis of silver nanoparticles (AgNPs), followed by their characterization using different techniques and evaluation of their potential as antioxidants, amylase inhibitors, anti-inflammatory and antibacterial agents. The formation of AgNPs was detected by a color change, from transparent to dark brown, within 15 min and a surface resonance peak at 460 nm in the UV–visible spectrum. The FTIR spectra confirmed the involvement of various biomolecules in the synthesis of AgNPs. The average diameter of these spherical AgNPs was 67 nm, as shown by the scanning electron micrograph. The inhibition zones showed that the synthesized nanoparticles inhibited the growth of Gram-positive and negative bacteria. FRAP and DPPH assays were used to demonstrate the antioxidant potential of AgNPs. The highest value of FRAP (50.47% AAE/mL) was detected at a concentration of 90 ppm and a DPPH scavenging activity of 69.63% GAE was detected at a concentration of 20 µg/mL of the synthesized AgNPs. 500 µg/mL of the synthesized AgNPs were quite efficient in causing 91.78% denaturation of ovalbumin. The AgNPs mediated by A. lycoctonum also showed an inhibitory effect on α-amylase. Therefore, AgNPs synthesized from A. lycoctonum may serve as potential candidates for antibacterial, antioxidant, anti-inflammatory, and antidiabetic agents.

A plant’s response to osmotic stress is a complex phenomenon that causes many abnormal symptoms due to limitations in growth and development or even the loss of yield. The current research aimed to analyze the agronomical, physiological, and biochemical mechanisms accompanying the acquisition of salt resistance in the Vigna radiata L. variety ‘Ramzan’ using seed osmo- and thermopriming in the presence of PEG-4000 and 4 °C under induced salinity stresses of 100 and 150 mM NaCl. Seeds were collected from CCRI, Nowshera, and sowing was undertaken in triplicate at the Department of Botany, Peshawar University, during the 2018–2019 growing season. Rhizospheric soil pH (6.0), E.C (2.41 ds/m), field capacity, and moisture content level were estimated in the present study. We observed from the estimated results that the agronomic characteristics, i.e., shoot fresh weight and shoot dry weight in T9 (4oC + 150 mM NaCl), root fresh weight and root dry weight in T4 (PEG + 100 mM NaCl), shoot moisture content in T5 (PEG + 100 mM NaCl), and root moisture content in T6 (PEG + 150 mM NaCl) were the highest, followed by the lowest in T1 (both shoot and root fresh weights) and T2 (shoot and root dry weights). Similarly, the shoot moisture content was the maximum in T5 and the minimum in T6, and root moisture was the highest in T6. We observed from the estimated results that agronomical parameters including dry masses (T4, T6, T4), leaf area index, germination index, leaf area, total biomass, seed vigor index under treatment T9, and relative water content and water use efficiency during T5 and T6 were the highest. Plant physiological traits such as proline, SOD enhanced by T1, carotenoids in treatment T2, and chlorophyll and protein levels were the highest under treatment T4, whereas sugar and POD were highest under treatments T7 and T8. The principal component analysis enclosed 63.75% of the total variation among all biological components. These estimated results confirmed the positive resistance by Vigna radiata during osmopriming (PEG) and thermopriming (4 °C) on most of the features with great tolerance under a low-saline treatment such as T4 (PEG), T5 (PEG + 100 mM NaCl), T7 (4 °C), and T8 (4 °C + 100 mM NaCl), while it was susceptible in the case of T6 (PEG + 150 mM NaCl) and T9 (4 °C + 150 mM NaCl) to high salt application. We found that the constraining impact of several priming techniques improved low salinity, which was regarded as economically inexpensive and initiated numerous metabolic processes in plants, hence decreasing germination time. The current study will have major applications for combatting the salinity problem induced by climate change in Pakistan.

Plants are sessile and mostly exposed to various environmental stresses which hamper plant growth, development, and significantly decline its production. Drought stress is considered to be one of the most significant limiting factors for crop plants, notably in arid and semi-arid parts the world. Therefore, the present study aimed to evaluate the potential impact of different concentrations (10, 100, and 200 µg/mL) of kinetin capped zinc oxide nanoparticles (Kn-ZnONPs) on Vigna radiata (L.) R. Wilczek under varying levels (5%, 10%, 15%) of PEG-induced drought stress. ZnONPs were synthesized by a co-precipitation method using Zinc acetate as a precursor at pH-12, incinerated to 500 °C, and kinetin was used as a surface functionalizing agent. The resulting Kn-ZnONPs were characterized by various contemporary analytical techniques, including SEM, SEM-EDS, XRD, DLS, and Zeta potential and IR spectroscopy. Crystalline Kn-ZnONPs, with a zeta potential of 27.8 mV and a size of 67.78 nm, of hexagonal wurtzite structure and vibrational stretches associated with N-H, C-O, C-N, etc., were confirmed. PEG-induced drought stress significantly reduced the growth of V. radiata by declining the chlorophyll and carotenoid contents. Moreover, a significant decrease in the levels of superoxide dismutase (SOD), peroxidase (POD), catalase (CAT), ascorbate peroxidase (APX), soluble sugar contents, proline, protein contents, phenol, and tannin were observed compared to the control. However, the exogenous application of Kn-ZnONPs ameliorated all photosynthetic parameters by up-regulating the antioxidant defense system through the promotion of SOD, POD, CAT, and lipid peroxidation levels. The biochemical parameters, such as proteins, soluble sugars, and proline, were observed to be maximum in plants treated with 200 µg/mL Kn-ZnONPs under 5% drought stress. The application of Kn-ZnONPs also enhanced the total phenol contents, flavonoid, and tannin contents. In conclusion, the findings of this study demonstrate that the exogenous application of Kn-ZnONPs provides beneficial effects to V. radiata by attenuating the damaging effects of drought stress through the up-regulation of the antioxidant defense system and osmolytes. These results suggest that Kn-ZnONPs have potential as a novel approach to improve crop productivity under drought stress conditions.