Water Usage Patterns Research Articles

Determination of peak hour and maximum daily factors on domestic usage patterns in the Special Region of Yogyakarta

Water use during peak hours and daily maximum are two interrelated terms in water usage patterns. Fluctuations in water use can be determined by planning standards, namely, estimates of peak hour and daily factors, so as to optimize air production and improve services. This study aims to calculate the maximum peak hour and daily factors. This research was carried out in the Special Region of Yogyakarta with a sampling technique carried out using a stratified proportional random sampling as many as 200 respondents. The results showed that the use of water at peak hours was 266.73 liters / day, which was in the morning between 06.00 and 08.00 am, while the maximum daily use of water is 774.09 litres/day which is on Sunday. The peak hour factor in the Special Region of Yogyakarta is 1.30 and the maximum daily factor is 1.26, when compared to the standard established by the Directorate General of Human Settlements, Ministry of Public Works, the peak hour factor is 1.5 while the daily maximum is 1.1. This means that the peak hour and maximum daily factors cannot be generalized in the Special Region of Yogyakarta.

Innovative Water Supply Network Pressure Management Method—The Establishment and Application of the Intelligent Pressure-Regulating Vehicle

The development of many intelligent technologies, such as artificial intelligence and the Internet of Things, has brought new opportunities for water industry intelligence. Based on intelligent pressure regulation technology, this paper built an intelligent management platform, designed an intelligent pressure-regulating device, and combined both to form an intelligent pressure-regulating vehicle (IPRV). The IPRV has the functions of developing a pressure-regulating scheme, equipment selection, pressure reduction potential analysis, etc. It can bring convenience to the field test of the water supply network. In the field test, an intelligent pressure-regulating device was used to obtain the network data in the pilot site called S-cell. After utilizing the intelligent management platform to analyze the measured data, the water usage pattern and pressure reduction potential of the S-cell were obtained, and an optimal pressure-regulating strategy was formulated. The water pressure at the critical node always met the water demand at the critical node during the field test. In addition, no complaints were received from other users. The results show that the IPRV is not only convenient for utility managers to make decisions on building pressure-reducing stations, but also meets user needs, realizing a win–win situation for both users and companies.

Water usage pattern in the aftermath of COVID-19

Maintenance of good health and avoiding a viral infection is the prime focus for an individual during COVID-19 pandemic. Water being a universal solvent is used widely to clean the disinfectants in public places and individual household level. This study was designed to find any change in the water consumption pattern among the households after the emergence of COVID-19. To study this, a questionnaire was prepared and sent to the respondents through Google Forms. Collected data was analysed using suitable statistical methods and the results indicate that there was a significant change in the consumption pattern of drinking water from cold to hot (37.98%) among the respondents and also with respect to the total water usage. As the disease is communicable in nature, more water is needed to clean and disinfect the surface areas, washing hands etc. This eventually has a significant burden on the water resources in countries where water is already deficient, like India. It is recommended to adopt water conservation practices/technologies at the individual level by means of rain water harvesting techniques or use of efficient water aerator taps etc. to reduce water consumption.

Assessing the spatial and temporal variability of greenhouse gas emissions from different configurations of on-site wastewater treatment system using discrete and continuous gas flux measurement

Abstract. Global emissions linked to wastewater treatment are estimated to account for up to 1.5 % of total greenhouse gas (GHG) emissions globally. However, few studies have measured GHG emissions from domestic on-site treatment systems (DWWTSs) directly. In this study, two DWWTSs were monitored for 446 d and > 42 000 gas flux measurements were conducted using both discrete spot measurements and continuous flux chamber deployments. The observed GHG fluxes from biological activity in the soil and water phase were found to be highly spatially and temporally variable and correlated to environmental factors, water usage patterns and system design. In total, the results show that a septic tank discharging effluent into a well-designed soil treatment unit is estimated to emit a net 9.99 kg-CO2eq.cap-1yr-1, with approximately 63 %, 27 % and 10 % of the total CO2-equivalent net emissions in the form of CO2, CH4 and N2O, respectively. Emissions from the septic tank surface contributed over 50 % of total emissions and tended to be strongly underestimated by one-off discrete measurements, especially when episodic ebullitive events are to be considered. Fluxes from the soil treatment unit (STU) stemmed from both the soil surface and the vent system. Soil fluxes were mostly influenced by temperature but peaked regularly under conditions of rapidly changing soil water content. Vent fluxes were mostly governed by effluent, quality and a low number of high-emission events were responsible for the majority of total observed vent emissions. Owing to the strong overall spatial and temporal heterogeneity of observed fluxes from DWWTSs across all modules, future studies should focus on continuous deployments of a number of flux chambers over discrete measurements to accurately assess GHG emissions from on-site systems. This study also provided insights into managing GHG emissions from DWWTSs by different system configuration design, as well as indicating that the current IPCC emission factors for CH4 and N2O significantly overestimate emissions for on-site wastewater treatment systems.

A Strategy to Assess Water Meter Performance

Apparent water losses can be problematic to water companies’ revenues. This type of loss is very difficult to detect and quantify and is often associated with water meter anomalies. This study was motivated by a water company’s challenge that links a decrease in water consumption to water meters’ malfunction. The aim is to develop a strategy to detect decreasing water usage patterns, contributing to meter performance assessment. The basis of the approach is a combination of statistical methods. First, the time series of billed water consumption is decomposed using Seasonal-Trend decomposition based on Loess. Next, breakpoint analysis is performed on the seasonally adjusted time series. After that, the Mann–Kendall test and Sen’s slope estimator are used to analyze periods of progressive decrease changes in water consumption, defined by breakpoints. A quantitative indicator of this change is proposed. The strategy was successfully applied to eight-time series of water consumption from the Algarve, Portugal.

Modelling and Incorporating the Variable Demand Patterns to the Calibration of Water Distribution System Hydraulic Model

Calibration of a water distribution system (WDS) hydraulic model requires adjusting several parameters including hourly or sub-hourly demand multipliers, pipe roughness and settings of various hydraulic components. The water usage patterns or demand patterns in a 24-h cycle varies with the customer types and can be related to many factors including spatial and temporal factors. The demand patterns can also vary on a daily basis. For an extended period of hydraulic simulation, the modelling tools allows modelling of the variable demand patterns using daily multiplication factors. In this study, a linear modelling approach was used to handle the variable demand patterns. The parameters of the linear model allow modelling of the variable demand patterns with respect to the baseline values, and they were optimised to maximise the association with the observed data. This procedure was applied to calibrate the hydraulic model developed in EPANET of a large drinking water distribution system in regional South Australia. Local and global optimisation techniques were used to find the optimal values of the linear modelling parameters. The result suggests that the approach has the potential to model the variable demand patterns in a WDS hydraulic model and it improves the objective function of calibration.

An application of GRACE mission datasets for streamflow and baseflow estimation in the Conterminous United States basins

Runoff and baseflow estimates provide critical information on surface and groundwater flow dynamics, water resource availability, and water usage patterns for effective water resource planning and management. Developing a physical-based model at large spatial scales such as continental/global scales require extensive datasets, complex parameterization, and higher computational capacity. Recent advances in satellite datasets related to water cycle process modeling, on the other hand, involve less parameterization and more effective computation over larger spatial and finer temporal scales. Evapotranspiration (ET) is a critical flux in the water cycle that can be estimated with reasonable accuracy from satellite-based information by using complementary relationship (CR) concepts. Similarly, the Gravity Recovery and Climate Experiment (GRACE) mission satellite datasets provide useful estimates of total water storage change over large spatial extents. GRACE datasets have been extensively used in groundwater storage change and groundwater depletion studies. However, the application of GRACE data products for estimating runoff and baseflow is limited. Therefore, in this study, we used a CR approach to estimate ET and a water balance method for estimating runoff and baseflow over the Conterminous United States (CONUS). We evaluated the CR-based water balance model (CR-GRACE) with United States Geological Survey (USGS) runoff datasets based on hydrologic unit code level-2 (HUC-2) basin boundaries. The CR-GRACE model estimated annual runoff values agreed with USGS runoff values with root mean square error (RMSE) and correlation coefficient values of 14.7 mm/month and 0.81, respectively. We also evaluated the CR-GRACE model with two well-known Land Surface Models (LSM), Noah-LSM and Variable Infiltration Capacity LSM (VIC-LSM), from the Global Land Data Assimilation System (GLDAS) database. The comparison results for baseflow estimates from the CR-GRACE model have improved over Noah-LSM and VIC-LSM model estimates with RMSE values of 62.16, 101.00, and 207.32 mm/year, respectively. Furthermore, this study demonstrates the feasibility of using the CR-GRACE model to estimate runoff and baseflows with limited parameterization and datasets.

A Hidden Markov Model and Fuzzy Logic Forecasting Approach for Solar Geyser Water Heating

Time-based smart home controllers govern their environment with a predefined routine, without knowing if this is the most efficient way. Finding a suitable model to predict energy consumption could prove to be an optimal method to manage the electricity usage. The work presented in this paper outlines the development of a prediction model that controls electricity consumption in a home, adapting to external environmental conditions and occupation. A backup geyser element in a solar geyser solution is identified as a metric for more efficient control than a time-based controller. The system is able to record multiple remote sensor readings from Internet of Things devices, built and based on an ESP8266 microcontroller, to a central SQL database that includes the hot water usage and heating patterns. Official weather predictions replace physical sensors, to provide the data for the environmental conditions. Fuzzification categorises the warm water usage from the multiple sensor recordings into four linguistic terms (None, Low, Medium and High). Partitioning clustering determines the relationship patterns between weather predictions and solar heating efficiency. Next, a hidden Markov model predicts solar heating efficiency, with the Viterbi algorithm calculating the geyser heating predictions, and the Baum–Welch algorithm for training the system. Warm water usage and solar heating efficiency predictions are used to calculate the optimal time periods to heat the water through electrical energy. Simulations with historical data are used for the evaluation and validation of the approach, by comparing the algorithm efficiency against time-based heating. In a simulation, the intelligent controller is 19.9% more efficient than a time-based controller, with higher warm water temperatures during the day. Furthermore, it is demonstrated that a controller, with knowledge of external conditions, can be switched on 728 times less than a time-based controller.

The impact of sampling approach and daily water usage on lead levels measured at the tap

There are many sampling approaches available for lead (Pb) in drinking water. Selecting the best approach for its intended use is critical. The objective of this work was to compare water Pb levels collected by multiple sampling approaches from a model home plumbing system (HPS) that included an old Pb service line (LSL), as a function of daily water usage. Specifically, flushed, direct LSL, sequential profile, random daytime (RDT), first draw, and manual composite samples were compared, and daily water usage rates ranged from 1.4 to 120 gallons/day (5.3–454.2 L/day). Pb levels in water collected directly from the LSL reached equilibrium after 7–15.5 h of stagnation, and approximately 55% of the equilibrium Pb concentration was reached after 2 h. Sequential sampling accurately identified the LSL; however, the peak profile Pb concentrations were a fraction of the Pb measured directly from the LSL. Daily water usage patterns greatly impacted total Pb levels in all water sampling approaches, although manual composite and RDT samples were more sensitive to changes. Manual composite and RDT samples were equal to or greater than first draw samples throughout the study, and differences grew larger as water usage decreased.

In-Water Antibiotic Dosing Practices on Pig Farms.

Pigs reared on many farms are mass-medicated for short periods with antibiotics through their drinking water to control bacterial pathogen loads and, if a disease outbreak occurs, to treat pigs until clinical signs are eliminated. Farm managers are responsible for conducting in-water antibiotic dosing events, but little is known about their dosing practices. We surveyed managers of 25 medium to large single-site and multi-site pig farming enterprises across eastern and southern Australia, using a mixed methods approach (online questionnaire followed by a one-on-one semi-structured interview). We found wide variation in the antibiotics administered, the choice and use of dosing equipment, the methods for performing dosing calculations and preparing antibiotic stock solutions, the commencement time and duration of each daily dosing event, and the frequency of administration of metaphylaxis. Farm managers lacked data on pigs’ daily water usage patterns and wastage and the understanding of pharmacology and population pharmacometrics necessary to optimize in-water dosing calculations and regimens and control major sources of between-animal variability in systemic exposure of pigs to antibiotics. There is considerable scope to increase the effectiveness of in-water dosing and reduce antibiotic use (and cost) on pig farms by providing farm managers with measurement systems, technical guidelines, and training programs.

The Household Water Usage Community Awareness Regarding Water Pollution and Factors Associated with it Among Adult Residents in MOH Area, Uduvil

Introduction: Water pollution is a one of the public health burdens and the consumption of contaminated water has adverse health effects and even affects fetal development. The objective was to describe the household water usage pattern, community awareness of water pollution and factors associated with it among adult residents in MOH area, Uduvil. Method: A descriptive cross-sectional study was conducted on a community-based sample of 817 adult residents with multi stage cluster sampling method. The data was collected by an int erviewer adm inistered questi onnaire. Statistically significance for selected factors and awareness were analyzed with chi square and Mann-Whitney U test. Results: This study sample consisted of 85% (n=695) female, mean age of 47.8±16.2. Majority 97.5% (n=797) of the participants knew about the water pollution in their area. Only 23% (n=188) participants knew about all the characters of good water. Among the participants 48.6% (n=397) had noticed the organoleptic changes in their own well and only 1.5% (n=12) had confirmed the pollution by lab test. Among them 71.2% (n=582) were treating the water and 24.9% were boiling the water. The median knowledge score was 10.93. There is significant difference in knowledge related to water pollution among different educational levels (U=53339.5, p<0.001) and the presence of water related diseases in the family (U=54146.5, P<0.01). There was a significant association with the age groups and their water treatment practices (χ2= 7.17, P<0.01), the presence of under 5-year age child in the family had significant association with water treatment practices (χ2=13.1, P<0.001).

Water Usage Patterns and Water Saving Devices in Households: A Case of Eastleigh, Nairobi

Water conservation measures are rapidly becoming a necessity in urban environments all over Africa and the world. There is little innovation and available information for homeowners, building professionals and practitioners to guide towards water conservation. Water scarcity has progressively increased over periods of time in Eastleigh due to increased population density from increased high-rise apartments. The study tries to establish how water usage patterns and water conservation devices can potentially reduce water consumption levels. In the study area, 96% of the families are faced with problems related to water supply and hence are forced to come up with water conservation and adaptation strategies due to the scarcity of water. The study used survey and descriptive research design, and collected data on water usage patterns and water conservation devices from a total of 230 households in Biafra and Sewage estates in the Eastleigh neighbourhood. The results indicated that 76% of the households did not have water saving devices while the other 24% had water saving devices such as the dual flush toilets, low flow high-efficiency faucet aerators, low flow plumbing fixtures and automatic shut-off nozzles. A unit increase in water saving devices will lead to a 0.512 decrease in water consumption level. The study recommends that low-income households use water saving devices and develop water management strategies such as water-saving plumbing fixtures, rainwater harvesting, and grey water reuse.

Predicting Urban Water Quality with Ubiquitous Data - A Data-driven Approach

Urban water quality is of great importance to our daily lives. Prediction of urban water quality help control water pollution and protect human health. However, predicting the urban water quality is a challenging task since the water quality varies in urban spaces non-linearly and depends on multiple factors, such as meteorology, water usage patterns, and land uses. In this article, we forecast the water quality of a station over the next few hours from a data-driven perspective, using the water quality data, and water hydraulic data reported by existing monitor stations and a variety of data sources we observed in the city, such as meteorology, pipe networks, structure of road networks, and point of interests (POIs). First, we identify the influential factors that affect the urban water quality via extensive experiments. Second, we present a multi-task multi-view learning method to fuse those multiple datasets from different domains into an unified learning model. We evaluate our method with real-world datasets, and the extensive experiments verify the advantages of our method over other baselines and demonstrate the effectiveness of our approach.

Morbidity profile of communities in Bhopal city (India) vis-à-vis distance of residence from Union Carbide India Limited plant and drinking water usage pattern.

Objective:A cross-sectional study was undertaken to assess the prevalence of morbidities in communities residing at variable distances from the closed down insecticide manufacturing plant premises of Union Carbide India Limited (UCIL), Bhopal, India and to determine association of morbidities, if any, with their drinking water usage pattern and distance of localities from the UCIL plant.Materials and Methods:A total of 10,827 individuals belonging to 2,184 families, residing within 0-1 km (Stratum I) and 2.5-5.0 km (Stratum II) radial distances from UCIL plant were surveyed and 9,306 of them (86%) were clinically examined. Data were analyzed to examine the association between the groups of morbidities, likely due to biological and chemical water contamination, and the distance of locality from the UCIL plant. Multiple logistic regression was used to explore the risk factors for morbidities.Results:Nearly similar prevalence (25.3% in stratum I, 25.8% in stratum II) and the trend of all-cause morbidities were recorded in the two strata. While morbidities related to gastrointestinal tract system (P < 0.05), auditory system (P < 0.01), neoplasm/cancers (P < 0.01) and congenital anomalies (P < 0.01) were significantly higher in stratum I, the prevalence of hypertension (6.4% stratum II, 4.7% stratum I; P < 0.01) and diabetes mellitus (3.4% stratum II, 2.0% stratum I; P < 0.001) was found significantly higher in stratum II. No association (P > 0.05) was observed between the prevalence of morbidities, likely due to the consumption of biologically or chemically contaminated drinking water, and the distance of locality/stratum from the UCIL plant.Discussion and Conclusion:By and large similar pattern of morbidities were recorded in the two strata suggesting that the communities, irrespective of the distance of their residences from UCIL plant or sources of their drinking water, are equally vulnerable to various morbidities.

A random forest model for inflow prediction at wastewater treatment plants

Influent flow of wastewater treatment plants (WWTPs) is a crucial variable for plant operation and management. In this study, a random forest (RF) model was applied for daily wastewater inflow prediction, and a new probabilistic prediction approach was, for the first time, applied for quantifying the uncertainties associated with wastewater inflow prediction. The RF model uses regression trees to capture the nonlinear relationship between wastewater inflow and various influencing factors, such as weather features and domestic water usage patterns. The proposed model was applied to the daily wastewater inflow prediction for two WWTPs (i.e., Humber and one confidential plant) in Ontario, Canada. For the confidential WWTP, the coefficient of determination (\(\varvec{R}^{2}\)) values for training and testing were 0.971 and 0.722, respectively. The \(\varvec{R}^{2}\) values at the Humber WWTP were 0.957 and 0.584 for training and testing, respectively. In comparison with other approaches such as the multilayer perceptron neural networks (MLP) models and autoregressive integrated moving average models, the results show that the RF model performs well on predicting inflow. In addition, probabilistic prediction of daily inflow was generated. For the Humber station, 93.56% of the total testing samples fall into its corresponding predicted interval. For the confidential plant, 78 observed values of the total 89 samples fall into its corresponding interval, accounting for 87.64% of the total testing samples. The results show that the probabilistic approach can provide robust decision support for the operation, management, and optimization of WWTPs.

Wireless water usage monitoring system for home / small premises

&lt;p&gt;The fourth Industrial Revolution has led to tremendous change in industrial automation. Measurement system can be seen as an important tool implemented in various fields because it enables us to access essential data from the environment or desired location. One of the essential measurement systems in industry, company or home is water usage monitoring. Water usage monitoring is the regular collection of information on the total amount of water drawn from sources during a given period. It enables a company or industry to understand water usage patterns and identify potential inefficiencies. For instance, a hotel premise who wants to monitor its water usage per room basis. Monitoring is also essential to set reduction targets of water used. The paper presents the development of wireless water usage monitoring system. This system consists of two nodes which are sensor node and sink node. The sensor node collects the water usage data and send them to the sink node. An ultrasonic sensor, Light-Emitting Diode (LED) and buzzer are attached to the sensor node as alert system for the user in case of water wastage occurrence. The sink node receives data from the sensor node wirelessly and mark this data time stamp by referring to a Real Time Clock (RTC) and store it in the database. The database is attached to sink node with Secure Digital (SD) card module. Furthermore, a Graphical User Interface (GUI) is used to display the water usage data in graphical form for easier user interpretation. The proposed wireless water usage monitoring system is suitable for home and small premises usage.&lt;/p&gt;

Development of an optimizing method for the operation rule curves of a multipurpose reservoir in a Southeast Asian watershed

Water scarcity and flooding constitute major problems for developing countries located within the tropical climatic region of Southeast Asia. In addition, regional water consumption is increasing, and water usage patterns have been changing recently. Therefore, an advanced water resource management framework that considers both water supply and flood control is needed. Multipurpose reservoirs are widely used to manage water resources efficiently; however, water-related problems occur with reservoirs in Southeast Asian watersheds because of inadequate operation rule curves. We developed a method for constructing optimal operation rule curves for Dau Tieng Reservoir, which is one of the largest multipurpose reservoirs in Vietnam. The reservoir is used for flood control, domestic water supply, industrial uses, environmental flows, and agricultural uses, in the order of priority. The operation rule curves of the Dau Tieng Reservoir comprise five reference water levels: the retarding water level, upper water level, lower water level, critical water level, and dead water level. Water release from the reservoir is determined based on the relationship between the reservoir level and the rule curves. In this study, the rule curves were newly determined using the shuffled complex evolution method of the University of Arizona (SCE-UA method). The objective function for optimization was defined by focusing on the improvement in insufficient supply for agricultural uses and environmental flow downstream of the Dau Tieng Reservoir. Inadequate solutions were prevented by introducing penalty functions into the objective function. Experimental results indicate that the proposed optimization method efficiently searches for optimal rule curves.

Using stable isotopes and tritium to delineate groundwater flow systems and their relationship to streams in the Geum River basin, Korea

This study aims to clarify groundwater and stream water flow systems in the Geum River basin, Korea, which is an area with a crystalline basement using δ18O, δ2H, and 3H. Stable isotopes of precipitation showed clear differences between dry and rainy seasons due to the Asian monsoon. Groundwater and stream water samples were collected in the dry and rainy seasons in three hydrographic zones of the upper, middle, and lower zones. The contribution of rainy season precipitation to groundwater recharge based on deuterium excess was 71–89%, far exceeding the amount fraction of the season in total precipitation. Stable isotopic compositions of groundwater and stream water showed little seasonal variation, and indicated the dominance of high-altitude recharge in the upper zone. Groundwater with lower 3H was mainly observed in that area, indicating a contribution of intermediate flow with a longer residence time. In contrast, stream water and some groundwater samples in the lower zone showed seasonal variability and signatures of 10–20% evaporation in their stable isotopic composition. The 3H content of groundwater in the lower zone was high, indicating that the local flow system is dominant. These regional features also influenced the relative contributions of rainy season precipitation, dry season precipitation, and local recharge with evaporation signature, as estimated from δ18O and deuterium excess values, and 3H in groundwater. Stream water from lower-order streams of the river had stable isotopic compositions similar to those in the upper zone, indicating a considerable contribution of groundwater discharge in the mountainous areas to the lower reach of the river. These results have implications for sustainable management of water resources in the basin during predicted changes in water usage and precipitation patterns due to climate change, as well as in regions with similar climatic and hydrologic conditions.

True exposure to lead at the tap: Insights from proportional sampling, regulated sampling and water use monitoring

This work aimed to evaluate the ability of four regulatory sampling protocols to accurately determine weekly water lead levels (WLLs) of exposure at the kitchen tap in twenty-nine households with or without a lead service line (LSL). Proportional sampling was used as the gold standard to which the other protocols, 5-min flush, 30-min stagnation, 6-h stagnation and random daytime were compared. Random daytime samples provided mean WLLs closest to true exposure in the households monitored overall compared to other sampling protocols. Strikingly, mean WLLs after 5 min of flushing underestimated lead exposure by 47%. Supporting these observations, water usage patterns revealed that full flushing only occurs in 3.4% of usage events within the service line and in 0.26% at the tap. The time between usage events in the service line was approximately 30 min but the 30-min protocol tended to slightly underestimate WLLs. These differences were explained by flushing prior to the 30-min stagnation sampling, which limited the contribution of the LSL to WLLs. Furthermore, the average stagnation at the kitchen time was 106 min and usage events rarely exceeded the water volume within premise plumbing (1.1 L). Mean WLLs after 6 h of stagnation without flushing overestimated exposure by 29% but provided a conservative indicator of WLLs of exposure.

Value of Sedum species as companion plants for nectar-producing plants depends on leaf characteristics of the Sedum

Introducing nectar-producing plants to green roofs and other urban green infrastructure has been attempted to support pollinator activities. Green roofs can be drought-prone, placing limitations on the success of plant growth and nectar production in the absence of irrigation. Plant species differ greatly in water usage patterns, so companion planting with low and high water users has been proposed to increase performance of high water use species, including species important for urban pollinators. Additionally, crassulacean acid metabolism (CAM) plants such as Sedum tend to have low water usage and are reported to improve health condition of neighbors. However, Sedum species differ in morphological and physiological traits such as leaf shapes and the speed at which they switch to CAM, resulting in water usage differences during drought that could affect neighboring plants. This study quantifies the effects of companion planting with CAM plants (Sedum album, Sedum kamtschaticum) on nectar-producing plants (Fagopyrum esculentum, Trifolium repens), focusing on target plant growth form and leaf traits of neighboring Sedum. The health condition of F. esuculentum and soil water contents were improved significantly when they were planted together with S. album, which has small leaves and rapid CAM induction. Therefore, suppression of evapotranspiration of neighboring CAM plants could have positive effects on erect growth form nectar species. In contrast, the health condition of T. repens, which shows prostrate growth, was not affected by neighboring CAM plant species. Judging from these results, growth characteristics including leaf traits of neighboring Sedum and growth forms of the target plants could be important factors determining the effects of companion planting in urban green infrastructure.