Improve Water Quality Research Articles

Trends in Water Quality Research: A Bibliometric Study of Factors and Parameters in Drinking Water

ABSTRACT This study examines trends in water quality research through a bibliometric approach with the aim of mapping dominant factors and parameters and identifying research gaps in this area. The introduction of this research highlights the importance of water quality as a global issue that affects public health and environmental sustainability. The main objective of the study was to analyze water quality-related publications published between 2017 and 2024 using the Publish or Perish application to identify trends, distribution, and research focus. The method used involved collecting data from the 50 highest-ranked journals on Google Scholar, then classifying them by year of publication, publication type, number of citations, and author country. The data obtained was analyzed with the VOSviewer application to generate network visualizations, overlays, and densities to reveal the linkages between research topics. The analysis showed that research on water quality is highly diversified with the main focus on factors affecting water quality and the development of assessment methods. The publications reviewed highlighted global challenges in water quality management and the need for cross-disciplinary collaboration. The conclusions of this study emphasize several research gaps, such as the need for the development of more sensitive water quality indices, the application of new technologies, and further research in local and regional contexts. This research provides valuable insights into current research trends and encourages further exploration to improve water quality management effectively and sustainably. Keywords: Water Quality, Bibliometrics, Assessment Methods, Emerging Technologies, Research Gaps.

Fluoride enrichment in groundwater and its association with other chemical ingredients using GIS in the Arjunanadi River basin, Southern India: Implications from improved water quality index and health risk assessment

Fluoride enrichment in groundwater and its association with other chemical ingredients using GIS in the Arjunanadi River basin, Southern India: Implications from improved water quality index and health risk assessment

Innovative lake pollution profiling: unveiling pollutant sources through advanced multivariate clustering techniques.

In many developed and developing nations, lakes are the primary source of drinking water. In the current scenario, due to rapid mobilization in anthropogenic activities, lakes are becoming increasingly contaminated. Such practices not only destroy lake ecosystems but also jeopardize human health through water-borne diseases. This study employs advanced hierarchical clustering through multivariate analysis to establish a novel method for concurrently identifying significantly polluted lakes and critical pollutants. A systematic approach has been devised to generate rotating component matrices, dendrograms, monoplots, and biplots by combining R-mode and Q-mode analyses. This enables the identification of contaminant sources and their grouping. A case study analyzing five lakes in Bengaluru, India, has been conducted to demonstrate the effectiveness of the proposed methodology. Additionally, one pristine lake from Jammu & Kashmir, India, has been included to validate the findings from the aforementioned five lakes. The study explored correlations among various physical, chemical, and biological characteristics such as temperature, pH, dissolved oxygen, conductivity, nitrates, biological oxygen demand (BOD), fecal coliform (FC), and total coliform (TC). Critical contaminants forming clusters included conductivity, nitrates, BOD, TC, and FC. Factor analysis identified four primary components that collectively accounted for 85% of the overall variance. Following identification of pollution hotspots, the study recommends source-based pollution control and integrated watershed management, which could significantly reduce lake pollution levels. Continuous monitoring of lake water quality is essential for identifying actual contaminant sources. These findings provide practical recommendations for maximizing restoration efforts, enforcing regulations on pollutant sources, and improving water quality conditions to ensure sustainable development of lakes.

Quality evaluation parameter and classification model for effluents of wastewater treatment plant based on machine learning

With the growing consensus of emerging pollutants and biological toxicity risks in wastewater treatment plant (WWTP) effluents, traditional water quality management based on general chemical parameters no longer meets the new challenges. Here, a first-hand dataset containing 9 conventional parameters, 22 mental and inorganic ions, 25 biotoxicity parameters, and 54 emerging pollutants from effluents of 176 municipal WWTPs across China were measured. Four clustering algorithms and five classification algorithms were applied to 65 well-performing models to determine a novel evaluation parameter system. A total of 14 parameters were selected by semi-supervised machine learning, including TN, TP, NH4+-N, NO2--N, Se, SO42-, Caenorhabditis elegans body width, 72 hpf zebrafish embryo hatching rate, tetracycline, acetaminophen, gemfibrozil, PFBA, PFHxA, and HFPO-DA. These parameters were then used to construct a Healthy Effluent Quality Index model (HEQi). The application efficiency of HEQi was compared with other common methods such as the Water Quality Index (WQI), Fuzzy Synthesized Evaluation (FSE), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) in classifying 176 effluents. Results implicated that under the new evaluation criteria, the major task in North and Northeast China remains to reduce the conventional parameters, especially NO2--N. However, it is necessary to strengthen the removal of biotoxicity and emerging pollutants in parts of Central and Eastern China. This study offers new methodological tools and scientific insights for improving water quality assessment and safe discharge of wastewater.

Adaptive denitrification adjustment by marimo for enhanced nitrate nitrogen removal in lake restoration

This work investigated the effectiveness of marimo in restoring lake ecosystems, with a focus on its capacity for adaptive denitrification. Observations revealed a significant increase in granule size, benefiting its settling capability and thereby facilitating substantial reductions in nitrate levels and overall lake water purification. Notably, nitrate nitrogen was identified as pivotal in the nitrogen cycle, exhibiting a strong coupling relationship with denitrification processes. Marimo autonomously adjusted its denitrification in response to environmental nitrate levels, enhancing its nitrogen removal efficiency. Furthermore, Rhodobacteraceae was found to play an important role in nitrogen metabolism, with competitive interactions between Proteobacteria and Bacteroidota, influencing the overall efficiency of nitrogen removal. It was found that at first, the accumulation of nitrate enhanced denitrification, while afterwards, the decrease in nitrate concentration weakened denitrification. In addition, assimilatory nitrate reduction showed an upward trend throughout the entire process. Analysis of metabolic pathways further supported the adaptive denitrification adjustment mechanism of marimo. These findings highlight the potential of marimo as a natural solution for water quality improvement in lake ecosystems.

Ecological health assessment using zooplankton index of biotic integrity (Z-IBI) in urban rivers in Hainan Island, China.

The Changwang, Meishe, and Wuyuan are urban rivers in Haikou City, Hainan Island. They are vital water sources, serving various purposes, from farmland irrigation to domestic and industrial use. Unfortunately, anthropogenic activities have influenced these "rivers," negatively impacting water quality and ecological functions; however, there is limited research on their ecological status. Thus, this study assessed the zooplankton community structure and developed the zooplankton index of biotic integrity (Z-IBI) to evaluate the ecological status. Samples were collected and analyzed seasonally in 2019. Generally, 159 zooplankton species belonging to 85 genera, 55 families, and 3 phyla were identified, dominated by Rotifer and Protozoa, followed by Arthropoda (groups of Copepod and Cladocera). The zooplankton density varied from 0.00 to 126.04 ind L-1, with averages of 19.49 ind L-1, 31.40 ind L-1, and 20.38 ind L-1 in Changwang, Meishe, and Wuyuan, respectively. The zooplankton density varied seasonally as summer > spring > winter > autumn. Analysis of similarity (ANOSIM) indicated a significant seasonal difference (R = 0.070, p < 0.001) in zooplankton community but not spatially (R = 0.004, p = 0.413). Thus, SIMPER revealed that Brachionus forficula, Didinium nasutum, Keratella valga, Nauplius, and Polyarthra vulgaris contributed to the seasonal differences. The redundancy analysis (RDA) revealed environmental factors, including total phosphorus (TP), total nitrogen (TN), ammonia nitrogen (NH3-N), chemical oxygen demand (CODMn), dissolved oxygen (DO), temperature, pH, Chl-a, and electrical conductivity (EC) influenced zooplankton species. The Z-IBI was established using Zooplankton abundance, Protozoa abundance, Copepod abundance, Cladoceran biomass %, and Shannon-Wiener diversity index. Based on Z-IBI evaluation, ecological status was "moderate" in Changwang and Wuyuan, while it was "low" in Meishe. Seasonally, the ecological status in autumn was better than in summer and spring. The Z-IBI showed that parameters, including pH, EC, CODMn, TN, TP, and Chl-a, decreased with improving water quality, while water transparency increased with improved water quality. Thus, the "rivers" ecological status could be adequately assessed using the Z-IBI to guide their water quality monitoring and management.

IoT Sensor Based Cross-Basin Natural Ecological Environment QualityMonitoring and Modeling Simulation with Artificial Intelligence RemoteSensing and GIS

This study presents an integrated approach for monitoring and modelling the quality of crossbasin natural ecological environments using advanced techniques such as Markov Random Field Clustering Classification (MRF-CC), Geographic Information Systems (GIS), Hidden Markov Models (HMM), and remote sensing. With key ecological parameters including water quality, biodiversity, habitat suitability, and land cover types across various scenarios and locations within a study area. The simulation results from MRF-CC revealed the significant impacts of different environmental scenarios and management actions, with restoration efforts showing improvements in ecological quality, while pollution mitigation and urbanization pressures led to declines. The simulation results from MRF-CC revealed the significant impacts of different environmental scenarios and management actions. For example, restoration efforts (Scenario 1) improved water quality (pH 7.5), biodiversity index (0.88), and habitat suitability (0.78) compared to the baseline values (pH 7.2, biodiversity index 0.85, habitat suitability 0.75). Conversely, pollution mitigation (Scenario 2) and urbanization pressure (Scenario 8) resulted in declines, with water quality dropping to pH 7.0 and 6.9, biodiversity indices to 0.82 and 0.81, and habitat suitability to 0.72 and 0.71, respectively. GIS estimation provided spatial insights into ecological parameters, revealing variability across different locations. For instance, Point A (forest) exhibited a water quality index of 0.78, while Point D (urban) showed a lower index of 0.54. The integration of HMM offered probabilistic predictions of land cover dynamics, with probabilities ranging from 0.85 for forest at Point A to 0.45 for urban land cover at Point D.

Identifying and testing adaptive management options to increase river catchment system resilience using a Bayesian Network model

AbstractThe cumulative impacts of future climatic and socioeconomic change threaten the ability of freshwater catchments to provide essential ecosystem services. Stakeholders who manage freshwaters require decision-support tools that increase their understanding of catchment system resilience and support the appraisal of adaptive management options to inform decision-making. Our research aims to test the ability of a Bayesian Network model to identify adaptive management scenarios and test their effectiveness across future pathways to 2050. Using the predominantly arable river Eden catchment (320 km2) in eastern Scotland as a case study, we invited stakeholders from multiple sectors to participate in a series of workshops aimed at addressing water quality issues and achieving good ecological status in the catchment both now and in the future. Our participatory methods helped stakeholders overcome multiple layers of complexity and uncertainty associated with future-focused water management. Outputs of a Bayesian Network model simulated both current and future catchment resilience to inform the identification of six management scenarios. The effectiveness of each management scenario was tested using the Bayesian Network model. Two adaptive management scenarios increased catchment resilience and helped achieve good ecological status; a ‘Best Available Technology’ scenario, including aerobic granular sludge treatment, and a management scenario focused on ‘Resource Centre’, including phosphorus recovery from wastewater treatment works and constructed lagoons for crop irrigation. Stakeholders were interested in a 'Nature Based' management scenario including options such as wetland wastewater treatment methods and rural sustainable drainage systems, which improved water quality in the catchment, but had lower certainty in achieving desired outcome. Findings led to a recognition that innovative and collaborative action was required to improve current and future freshwater conditions.

Multi-Channel Stormwater Ponds: A Sustainable Approach to Urban Flood Resilience

Flood-prone cities depend on stormwater ponds, but current designs with single, shallow channels limit storage. This paper proposes a new approach: using multiple channels to create smaller sections on the pond floor. Modeling shows this significantly increases storage for floods without needing a bigger pond. It also improves water flow and reduces stagnant areas. Finally, the paper explores practicalities for building these ponds, making them a valuable tool for better urban water management without needing more land or changing the pond’s height. These multi-channel ponds are particularly beneficial for frequent smaller storms by distributing water flow, reducing its speed, and allowing more time for sediment to settle, leading to improved water quality. Sustainable solutions are essential for safeguarding the future. In this research, sustainability plays a pivotal role, as the multi-channel stormwater pond design provides an innovative approach to improving flood management without the need for extra land or resources, thus supporting the goals of sustainable urban development.

Machine Learning Techniques for Monitoring Water Quality in Water Resources

More than 66% of the Earth's total surface area is covered by water. Clean water is one of the basic needs of everyday life. Consistent pollution of water bodies can have far-reaching effects on the lives of living organisms. The World Health Organization has reported that the provision of safe drinking water for human consumption is a challenge that has reached alarming levels. This is because nearly 70% of the total water withdrawals worldwide are used in agriculture. To determine the water's suitability for human consumption, Tests are typically conducted by examining the properties of water in terms of physical, biological, and chemical conditions. There are various methods to measure water quality. Recently, an ongoing process has been shown to improve water quality. To solve this problem, this paper is using a machine learning model to Contribution for creation a smart model capable of classifying potable and non-potable water that is released to the water share by the competent authorities and the ability of Machine Learning models to monitor and predict water, especially Managing and Planning Water Resources. datasets were utilized in training and evaluating machine learning models, which includes (3276) samples with nine attributes and two labels indicating water usability According to the in our work the (Random Forest) algorithm have the best Through the results that appeared by accuracy (0.954084, 0. 882484, and 0. 931849 for three sizes of data.) and then (Decision Tree, K-Nearest Neighbor, Logistic Regression, SVC) sequentially. Index Terms—Machine Learning, potable water, Water Quality, water resources.

Enhanced aqueous phosphorus removal and mechanism by water spinach (Ipomoea aquatica Forsk) pretreated with lanthanum nitrate.

Excess nutrients such as phosphate (PO43-) entering surface waters promote eutrophication, and phosphorous (P) removal is important to clear the water. Phytoremediation efforts have been used to improve water quality by varieties of P removal plants, such as water spinach (Ipomoea aquatica Forsk). Water spinach can reduce both internal and external resources of phosphorus from waterbody. The ion of lanthanum (La), one rare earth element (REE), is an immobilization substance for aqueous phosphate and also a fertilizer for plants. Therefore, lanthanum nitrate La (NO3)3 was used further to improve the phytoextraction of P from the polluted water. This study investigated the effects of La on the aqueous P removal by two genotypes of water spinach, green stem large leaves (GSLL) and green stem willow leaves (GSWL). The low concentration La (NO3)3 helped the plant to remove more phosphorous from eutrophic water, but La at high concentration lowered the removal of P. Under La (NO3)3 treatments, the optimum concentration for maximum P removal in GSLL is 3mg/L, and for GSWL, it is 10mg/L and P removal rates were enhanced to 95% and 96%, respectively. When the concentration of La (NO3)3 is 100mg/L, the removal percentage of P was only 10% for both genotypes. The very high concentration of La will impose toxicity and even cause the death of the water spinach and produce secondary pollution; for example, under some specific circumstances, the bond between lanthanum and nitrates dissociates into lanthanum ions (La3⁺) and nitrate ions (NO₃⁻). If the concentration is high, then it accumulates in the aquatic water organisms and plants and causes toxicity in their bodies. If humans eat up these plants and fish, it causes toxic effects in humans. The La (NO3)3 positively affects different parameters of plants. La (NO3)3 increases the growth, pigments, enzyme activity, and malondialdehyde (MDA) of plants which were also discussed in this study. The biological mechanism should be responsible for the enhanced aqueous phosphorus removal by water spinach using lanthanum nitrate.

Water quality assessment of Upper Ganga and Yamuna river systems during COVID-19 pandemic-induced lockdown: imprints of river rejuvenation.

Clean river water is an essential and life-sustaining asset for all living organisms. The upper Ganga and Yamuna river system has shown signs of rejuvenation and tremendous improvement in the water quality following the nationwide lockdown due to the coronavirus pandemic. All the industrial and commercial activity was shut down, and there was negligible wastewater discharge from the industries. This article addresses the water quality assessment from the study area, which is based on the original data of physical parameters, major and trace elements, and stable isotopes (hydrogen and oxygen) systematics during the nationwide lockdown. The impact of the lockdown could be seen in terms of an increase in dissolved oxygen (DO). Water samples were collected from the Upper Ganga and Yamuna river basins (Alaknanda, Bhagirathi, and Tons rivers) during an eight-week lockdown in Uttarakhand, India. We discussed the signs of rejuvenation of riverine based on physical parameters, major ions, trace elements, isotopic ratios, and water pollution index (WPI). Results reveal that the water quality of the entire upper basins of the Ganga has significantly improved by 93%, reflecting the signs of self-rejuvenation of the rivers. Multivariate analysis suggests a negative factor loading for an anthropogenic element ( ), implying that they contribute little to the river water during the lockdown. Further, bicarbonate ( ) is a dominant element in both river basins. The geochemical facies are mainly characterized by the ( ) type of water, suggesting that silicate rock weathering dominates with little influence from carbonate weathering in the area. The positive factor loadings of some cations, like , , and reflect their strong association with the source of origin in the lockdown phases. Stable isotopic reveals that the glaciated region contributed the most to the river basin, as evidenced by the low d-excess in riverine water compared to anthropogenic contributions. Rivers can self-rejuvenate if issues of human influence and anthropogenic activities are adequately resolved and underline our responsibility for purifying the ecosystem. We observed that this improvement in the river water quality will take a shorter time, and quality will deteriorate again when commercial and industrial activity resumes.

Sensing volatile organic compounds in aquatic samples: a review

ABSTRACT Volatile organic compounds (VOCs) are toxic and harmful to human health; thus, their detection in water samples is critical for ensuring safe drinking water and protecting public health. The aim of this review was to explore the various techniques available in the literature for detecting VOCs in water samples and discuss their features, advantages, and limitations in facilitating fast and rapid VOC analysis. Established techniques such as gas chromatography and mass spectrometry offer high sensitivity and selectivity but are often hindered by complexity and cost. In contrast, emerging technologies like microfluidic gas sensors and cantilever-based sensors provide portable and real-time monitoring capabilities, although they may be susceptible to matrix effects. Overcoming these challenges requires advanced sample preparation techniques and method optimization. Additionally, the integration of machine learning algorithms and nanotechnology shows promise for enhancing sensitivity and precision in the detection of VOCs. By overcoming these limitations and leveraging technological advancements, researchers can improve water quality monitoring and help protect the environment.

Farmers' cooperation to improve water quality under scientific uncertainty: A lab‐in‐the‐field experiment

Farmers' cooperation to improve water quality under scientific uncertainty: A lab‐in‐the‐field experiment

Sorbents Based on Natural Zeolites for Carbon Dioxide Capture and Removal of Heavy Metals from Wastewater: Current Progress and Future Opportunities

This review is dedicated to the potential use of natural zeolites for wastewater treatment and carbon dioxide capture. Zeolites, due to their microporous structure and high surface activity, are used as sorbents. One effective application of zeolites is in wastewater treatment, which leads to the removal of pollutants and improvement in water quality. Zeolites can also be used for carbon dioxide capture, which helps reduce its concentration in the atmosphere and addresses climate change issues. This review examines recent research on the use of natural zeolites for the removal of heavy metals from water and CO2 capture. It explores the broad applications of natural zeolites by understanding their adsorption capabilities and the mechanisms affecting their performance in water purification from heavy metals and CO2 capture.

Mixed contaminant exposure in tapwater and the potential implications for human-health in disadvantaged communities in California

Water is an increasingly precious resource in California as years of drought, climate change, pollution, as well as an expanding population have all stressed the state's drinking water supplies. Currently, there are increasing concerns about whether regulated and unregulated contaminants in drinking water are linked to a variety of human-health outcomes particularly in socially disadvantaged communities with a history of health risks. To begin to address this data gap by broadly assessing contaminant mixture exposures, the current study was designed to collect tapwater samples from communities in Gold Country, the San Francisco Bay Area, two regions of the Central Valley (Merced/Fresno and Kern counties), and southeast Los Angeles for 251 organic chemicals and 32 inorganic constituents. Sampling prioritized low-income areas with suspected water quality challenges and elevated breast cancer rates. Results indicated that mixtures of regulated and unregulated contaminants were observed frequently in tapwater throughout the areas studied and the types and concentrations of detected contaminants varied by region, drinking-water source, and size of the public water system. Multiple exceedances of enforceable maximum contaminant level(s) (MCL), non-enforceable MCL goal(s) (MCLG), and other health advisories combined with frequent exceedances of benchmark-based hazard indices were also observed in samples collected in all five of the study regions. Given the current focus on improving water quality in socially disadvantaged communities, our study highlights the importance of assessing mixed-contaminant exposures in drinking water at the point of consumption to adequately address human-health concerns (e.g., breast cancer risk). Data from this pilot study provide a foundation for future studies across a greater number of communities in California to assess potential linkages between breast cancer rates and tapwater contaminants.

Decontamination Potential of Ultraviolet Type C Radiation in Water Treatment Systems: Targeting Microbial Inactivation

Access to safe water and sanitation is a critical global challenge, posing significant health risks worldwide due to waterborne diseases. This study investigates the efficacy of ultraviolet type C radiation as a disinfection method for improving water quality. The research elucidates UV-C’s mechanism of action, highlighting its ability to disrupt DNA and RNA replication, thereby inactivating pathogens. Furthermore, the study analyses the influence of key factors on UV-C disinfection effectiveness, including water turbidity and the presence of dissolved organic matter, which can attenuate UV-C penetration and reduce treatment efficiency. The experimental results demonstrate a substantial reduction in microbial content following UV-C treatment. River water samples exhibited a 57.143% reduction in microbial load, while well water samples showed a 50% reduction. Notably, Escherichia coli (E. coli) concentrations decreased significantly, with an 83.33% reduction in well water and a 62.5% reduction in borehole water. This study makes a novel contribution to the understanding of UV-C disinfection by identifying the presence of resistant organisms, including Adenoviruses, Bacterial spores, and the Protozoan Acanthamoeba, in water samples. This finding expands the scope of UV-C research beyond easily culturable bacteria. To address this challenge, future investigations should explore synergistic disinfection strategies, such as combining UV-C treatment with advanced oxidation processes. Optimising UV-C system designs and developing robust, real-time monitoring systems capable of detecting and quantifying known and emerging UV-resistant pathogens are crucial for ensuring comprehensive water decontamination.

Detection and Measurement of Bacterial Contaminants in Stored River Water Consumed in Ekpoma

This study was conducted in Ekpoma, a town dependent on rainwater and river water from nearby areas because of a lack of groundwater sources, and the physicochemical and bacteriological (heterotrophic plate count [HPC], total coliform count [TCC], and fecal coliform count [FCC]) properties of 123 stored river water samples grouped into five collection districts (EK1 to EK5). The results were compared with regulatory standards and previous regional studies to identify water quality trends. While most physicochemical properties met drinking water standards, 74% of samples had pH values &gt; 8.5. Twenty-seven samples were fit for drinking, with EK4 having the highest number of bacterio-logically unsuitable samples. Ten bacterial species were identified, with Gram-negative short-rod species such as Escherichia coli, Klebsiella pneumoniae, and Salmonella typhimurium being predominant. HPC values varied from 367 × 10⁴ to 1320 × 10⁴ CFU/mL, with EK2 (2505 × 10⁴ CFU/mL) and EK5 (1320 × 10⁴ CFU/mL) showing particularly high counts. The TCC values ranged from 1049 × 10⁴ to 4400 × 10⁴ CFU/mL, and the FCC values from 130 × 10⁴ to 800 × 10⁴ CFU/mL, all exceeding WHO limits (1.0 × 102 CFU/mL). Historical data show no improvement in water quality, emphasizing the need for individuals to treat water properly before consumption. The findings provide baseline data for local water authorities and serve as a wake-up call for adequate water treatment, storage interventions, and community education on water security. Additionally, this study offers a practical process for improving the quality of water stored in similar regions.

Perception-Based Assessment of the Water Supply System in Bhimdatta Municipality, Sudurpashchim Province, Nepal

Safe and adequate water supply is one of the serious and significant challenges for local governments in Nepal. The main objective of this study is to explore the community perception on existing water supply systems, its quality, and quantity alongside with associated problems in the Bhimdatta Municipality. The study adopted qualitative and quantitative methods with Systematic random sampling and purposive sampling techniques for data collection in 2024. A total of 379 households were selected across the 19 wards of municipality. The findings reveals that 44.9% of households use groundwater as their primary drinking water source, 31.9% rely on public supply, and the rest use both sources. The majority of respondents rated the water quality as good with 56.2%, while 26.4% rated as excellent, 10.8% as fair, 6.1% as poor, and 0.5% as very poor. Most of the respondents reported a lower incidence of diseases stemming from drinking water source, they noted problems such as taste, odor, turbidity, and color. Over the past decade, groundwater levels have declined, making access more difficult across all wards. This study provides vital insights for policymakers and stakeholders to address these challenges and improve water quality and accessibility.

Tree Planting Activity to Nurture Impactful Mission (Tanim) at Select Barangay in San Jose, Batangas

This study aimed to evaluate the impact of Tree Planting Activity to Nurture Impactful Mission (TANIM) at select barangay in San Jose, Batangas. The study design is descriptive-qualitative in nature. Based on the qualitative data analysis, two themes have emerged such as simple yet useful and the short-term and long-term ecological benefits. The respondents felt contentment and fulfillment with themselves and felt a sense of accomplishment as described as a sense of success and service from the heart which implied a profound sense of achievement and a heartfelt commitment to making a positive impact on the environment. The short-term ecological benefits as described by the participants were local biodiversity, soil protection, soil health improvement, air quality improvement, and microclimate regulation while the potential long-term ecological benefits were enhanced biodiversity, carbon sequestration, water quality improvement, climate resilience, nursery operations, seed collection and processing, community engagement, aesthetic and recreational value of planting more trees, and educational opportunities which implied the immediate gains and envisioned the lasting advantages. The output of the study is the action to increase participation in the Tree Planting Activity to Nurture Impactful Mission (TANIM).