Predict Infiltration Rates Research Articles

Introducing a tailored site delineation approach to optimize the design of managed aquifer recharge surface spreading infrastructure

Managed aquifer recharge (MAR) is an emerging solution to effectively replenish overused groundwater resources, but high associated costs often hinder its uptake. There are several parameters to consider when determining the cost of MAR, including recharge types, scale of MAR schemes, land acquisition, operation and maintenance period, and the hydrogeological setting. Hydraulic conductivity and its spatial variability are the most significant hydrogeological parameters for predicting infiltration rates at MAR sites, but limited data availability makes accurate predictions often challenging. Hence, it is essential to increase data availability to optimize MAR efficiency and to better assess its economic performance. Therefore, a novel approach for MAR planning is presented in this article to efficiently enhance the data availability during the conceptual stage of MAR planning by combining site exploration, spatially resolved infiltration rate estimation, and cost analysis. The approach was applied at an actual MAR site in Vincenza, Italy, and incorporated an electromagnetic induction survey and direct push profiling as across-scale investigation methods to enhance site delineation and subsequent parameterization of the identified zones using laboratory analysis of collected samples. This MAR site is particularly suitable for its heterogeneous subsurface structure and potential to counteract groundwater depletion. The performed investigation resulted in an image of the lateral variation of subsurface conditions and allowed the delineation of three zones with different infiltration behavior, with 99.5% of infiltration occurring in just two zones, representing only 22.1% of the site. In this particular scenario, the overall cost per cubic meter of recharged water can be reduced by 59.1% by identifying and eliminating unfavorable zones. This study provides scientists and practitioners with a useful tool for MAR planning that can be applied to a wide range of sites with complex geology, thereby reducing water costs, minimizing the environmental impact of infrastructure construction, and reducing land use conflicts.

Soil Infiltration Rate Prediction using Machine Learning Regression Model: A Case Study on Sepinggan River Basin, Balikpapan, Indonesia

The infiltration rate of soil data is important in a wide range of planning, such as city planning, drainage design, landuse planning, flood prediction, flood disaster mitigation, etc. Collecting data of infiltration through in-site direct measurements is time consuming and costly. Indeed, inferring the infiltration rate using available parameters and the fittest model is needed. The model can shortcut the field measurement to get a predicted accurate infiltration rate that is worthy to support vital planning. This research aims to develop a model of infiltration rate based on initial water contents and grain size of soils. The results are three outstanding models based on the Multiple R Squared, Root Mean Square Error (RMSE), and Mean Average Error (MAE). The implication of the fittest model is reducing the cost and time to get the predicted infiltration rate. The field measurements can be skipped by sampling undisturbed soils and laboratory tests. Keywords: infiltration rate, initial water contents, grain size

3D hydrogeophysical characterization of managed aquifer recharge basins

Aquifers are increasingly stressed. Managed aquifer recharge provides a potential solution to mitigate this stress and provide sustainable groundwater resources. Subsurface properties are known to have a strong control on the infiltration rates that can be achieved. However, these properties are often highly heterogeneous and difficult to assess with conventional probing techniques. Here, we show the application of 3D geophysical imaging to assess the recharge potential and its variation across several basins used for managed aquifer recharge. We link in-situ measurements of saturated hydraulic conductivity with the electrical resistivity of the subsurface to establish petrophysical relationships and use those relationships to estimate the distribution of hydraulic conductivity throughout the five recharge basins. Our results show a considerable variability in the hydraulic properties, i.e., soil texture and saturated hydraulic conductivity, that have a direct impact on potential infiltration rates. We use the 3D hydraulic property distributions to model groundwater recharge and provide estimates for infiltration rates and volumes, and use this approach to assess the impact of management activities on groundwater recharge performance. Having such data not only enables us to predict infiltration rates, but also provides means for optimizing such water infrastructure.

Evaluation of furrow infiltration by Swartzendruber and Horton’s models in Northern Guinea savanna of Nigeria

The study aimed at evaluating two models (Swartzendruber and Horton) using furrow infiltration data measured in Samaru, Zaria. These measurements were carried out on the three field plots A, B and C. Infiltration parameters were generated from the data measured from plot A and B and fitted into the models for predictions of water infiltrated depths at different time intervals. The predictions from the models were compared with the measured field data from plot C. Statistical indices such as coefficient of determination (R2), root mean square error (RMSE) and T-test at 5% level of significance were used to determine the best performing model. The results show that, value of R2 and RMSE recorded for Horton’s model was 0.996 and 3.05, while the value of R2 and RMSE recorded for Swartzendruber’s model was 0.998 and 3.01, respectively. The T-test values obtained for Horton’s model was 2.93, while 2.51 was recorded for Swartzendruber’s model. The results of the evaluation indicated that both models were considered suitable because they presented high values of R2 and low values of RMSE as suggested by Aminu (2019). Similarly, the results obtained from the T-test statistics indicated that there is a no significant difference between the models evaluated because the calculated values of ‘T’ 2.93 and 2.51were greater than the tabulated value 2.365 at 5% confidence interval. Therefore, this study recommends that both models should be used to predict infiltration rates of soils in the study area.

Evaluating methods for estimating whole house air infiltration rates in summer: implications for overheating and indoor air quality

PurposeAccurate values for infiltration rate are important to reliably estimate heat losses from buildings. Infiltration rate is rarely measured directly, and instead is usually estimated using algorithms or data from fan pressurisation tests. However, there is growing evidence that the commonly used methods for estimating infiltration rate are inaccurate in UK dwellings. Furthermore, most prior research was conducted during the winter season or relies on single measurements in each dwelling. Infiltration rates also affect the likelihood and severity of summertime overheating. The purpose of this work is to measure infiltration rates in summer, to compare this to different infiltration estimation methods, and to quantify the differences.Design/methodology/approachFifteen whole house tracer gas tests were undertaken in the same test house during spring and summer to measure the whole building infiltration rate. Eleven infiltration estimation methods were used to predict infiltration rate, and these were compared to the measured values. Most, but not all, infiltration estimation methods relied on data from fan pressurisation (blower door) tests. A further four tracer gas tests were also done with trickle vents open to allow for comment on indoor air quality, but not compared to infiltration estimation methods.FindingsThe eleven estimation methods predicted infiltration rates between 64 and 208% higher than measured. The ASHRAE Enhanced derived infiltration rate (0.41 ach) was closest to the measured value of 0.25 ach, but still significantly different. The infiltration rate predicted by the “divide-by-20” rule of thumb, which is commonly used in the UK, was second furthest from the measured value at 0.73 ach. Indoor air quality is likely to be unsatisfactory in summer when windows are closed, even if trickle vents are open.Practical implicationsThe findings have implications for those using dynamic thermal modelling to predict summertime overheating who, in the absence of a directly measured value for infiltration rate (i.e. by tracer gas), currently commonly use infiltration estimation methods such as the “divide-by-20” rule. Therefore, infiltration may be overestimated resulting in overheating risk and indoor air quality being incorrectly predicted.Originality/valueDirect measurement of air infiltration rate is rare, especially multiple tests in a single home. Past measurements have invariably focused on the winter heating season. This work is original in that the tracer gas technique used to measure infiltration rate many times in a single dwelling during the summer. This work is also original in that it quantifies both the infiltration rate and its variability, and compares these to values produced by eleven infiltration estimation methods.

Estimation of the real-time infiltration rate using a low carbon dioxide concentration

The real-time infiltration rate in a building is an important factor in controlling ventilation for energy saving and achieving acceptable indoor air quality. Recently, the occupant-generated CO2 tracer gas method has received considerable attention in that there is no need to inject a tracking gas and it can be easily calculated real-time infiltration rate through indoor and outdoor CO2 monitoring. However, data noise at low CO2 concentrations and a small drop in indoor concentrations during unoccupied periods can increase uncertainty in predicting infiltration rates. Besides, using an arbitrary reference value as the background CO2 concentration can lead to over or underestimation of the predicted values. The purpose of this study is to present an alternative form of the mass-balance equation for determining real-time infiltration rates and to review the issues that need to be considered in actual calculations. Field measurements were performed in a test room where four people work intermittently. In this study, an appropriate time interval setting method that exceeds the measurement uncertainty limit of the device is proposed, and a moving average technique is used to suppress noise signals in a low CO2 concentration. In addition, it is recommended to use the hourly average value of outdoor concentration and data with an indoor CO2 concentration higher than 1000 ppm. The originality of this work is to quantitatively analyze the uncertainty effect of outdoor carbon dioxide concentration information and to provide clear guidelines for determining the valid data interval carbon dioxide concentrations in predicting infiltration rates.

Evaluating potential benefits of air barriers in commercial buildings using NIST infiltration correlations in EnergyPlus

According to the U. S. Department of Energy (DOE), infiltration accounts for 6% of the energy use and $11 billion in energy cost for U. S. commercial buildings. One strategy to reduce infiltration in commercial buildings is to provide more supply airflow than return and exhaust in order to “pressurize the building”. DOE has developed EnergyPlus models of several prototype buildings which assume that pressurization results in system-on infiltration rates that are 75% less than the system-off rates. However, airflow simulations of these buildings using the CONTAM multizone airflow software showed that pressurization reduced infiltration by an average of 44% only. To improve the infiltration rates calculated by the EnergyPlus models of prototype buildings, CONTAM infiltration rates were used to develop coefficients that can be input into EnergyPlus. CONTAM captures the effects of wind, temperature difference, and system operation on infiltration rates. Coefficients were developed for 11 prototype buildings, eight cities, and two levels of building envelope airtightness. Comparisons of the predicted infiltration rates were made between using the DOE prototype model inputs and the NIST infiltration correlations. Using the NIST correlations resulted in an average HVAC-EUI (HVAC-related energy use intensity) savings of 6% or 1.4 kBtu/ft2 due to airtightening. These results indicate that the effects of infiltration on HVAC energy use are important and that infiltration can and should be better accounted for in whole-building energy modeling.

Evaluation of different infiltration models under long term conservation agricultural practices

A study was undertaken to evaluate the long term effect of different conservation agriculture (CA) practices on infiltration characteristics of soil and empirical Kostiakov model and physical process based Green and Ampt, and Philip models were used to predict infiltration rates. The performance of different models was evaluated using statistical criteria. Six treatments were selected, viz. conventional tillage (CT), permanent narrow bed (PNB), permanent narrow bed with residue (PNB+R), permanent broad bed (PBB), permanent broad bed with residue (PBB+R) and zero tillage (ZT). Results showed that the initial infiltration rate was highest (22.93 cm hr-1) in PBB+R and was lowest (7.64 cm hr-1) in CT. Cumulative infiltration of all the treatments followed the order: PBB+R>PNB+R>PBB>PNB>ZT>CT. The ‘a’ values of Kostiakov model was 197.5 and 310 % higher in PBB and PBB+R as compared to CT. Steady state infiltration (ic) of Green and Ampt model was found to be highest (8.47 cm hr-1) in PBB+R and was lowest (1.88 cm hr-1) in CT. Sorptivity (S) parameter (cm hr-0.5) of the Phillip model was found to be highest in PNB+R (67.33) followed by PBB+R (43.61) and lowest in CT (16.23). Highest saturated hydraulic conductivity (‘K’) value of Phillip model was obtained in PBB+R followed by PBB> PNB>PNB+R>CT. After checking the model performance, it has been found that simple empirical Kostiakov (1932) infiltration model represented the infiltration rate and time relationship in a better way and characterized the best fit with the experimentally observed field infiltration data.

Modelling infiltration rates in permeable stormwater channels using soft computing techniques*

AbstractIn the design of permeable stormwater channels, the ability to quantify infiltration rates accurately is important for assessing the capability of such channels to perform their required functions. Most of the available infiltration models neglect the effects of water level and channel section on the infiltration rate. In this study, physical channel models, with different channel sections, were developed in the laboratory and used to measure the infiltration rates. The performance of three soft computing techniques, including Gaussian process regression, M5P, and random forest (RF) models, were evaluated against measured values. Seven independent input variables, namely, channel side slope (m), base width (b), water level (y), sand (%), silt (%), clay (%), and time (T) and the output variable infiltration rate (f(t)), were considered in the model development and validation. The Gaussian progression–Pearson VII universal kernel function model approach was found to perform best for the data set considered, followed by the RF‐based model. The sensitivity investigation showed that time, water level, and channel side slope were the most influential input variables in predicting infiltration rates for permeable stormwater channels and should be given primary consideration in designing such channels. © 2020 John Wiley & Sons, Ltd.

Predicting micro-catchment infiltration dynamics

Rainfall and concentrated flow experiments were carried out on seven micro-catchments (MCs) that were designed to limit soil erosion and allow for water-harvesting. Prediction of infiltration rates within MCs is necessary to design effective hillslope-scale restoration projects. Continuous stage measurements and 3-D models of MC geometry were used to calculate infiltration rates from field experiments. Soil samples and Guelph permeameter (GP) measurements were collected to parameterize a predictive infiltration model in Hydrus 2D/3D. The model result of water velocity into the soil profile was averaged by depth intervals and multiplied by the corresponding MC surface area to calculate a volumetric flow rate. Four parameterizations of changes in conductivity with depth were evaluated within the model framework to determine which would best account for spatial heterogeneity. Use of the maximum field-measured conductivity provided the least biased results, with average error between simulated and measured values across all sites of less than 1%. Model results illustrate the limitations associated with particle-size distribution or GP measurements when used to predict infiltration rates in a numerical model. GP measurements with single ponded heights allowed convenient field measurement of conductivity that worked better than predictions from soil texture. The maximum of several GP samples was more representative of MC infiltration than the mean, so a higher percentile value from a distribution of MC measurements may help to account for complex infiltration processes that are not included in numerical models. This modeling approach will allow testing of process-based hypotheses about rangeland infiltration dynamics, and the development of optimal configurations of MCs at sites being considered for rangeland restoration.

Modified models for better prediction of infiltration rates in trapezoidal permeable stormwater channels

ABSTRACTIn stormwater management, it is important to accurately quantify the infiltration rates to solve urban runoff-related problems. This study proposes a method to improve estimates of the infiltration rate in permeable stormwater channels. As part of the analysis, five infiltration models were evaluated: the Kostiakov, Horton, modified Kostiakov, Philip and SCS (Soil Conservation Service) models. Infiltration tests with various initial water levels were performed on channel models with differing base width and side slopes. The results show that the addition of three parameters that describe the trapezoidal cross-sectional area, i.e. the depth, side slope and base width, in the infiltration models yielded better estimates of the infiltration rate. A comparison of the infiltration capacity values obtained from the models after the three parameters were added with those that were experimentally obtained, shows that the improved modified Kostiakov model is the most suitable model to predict infiltration rates in trapezoidal permeable stormwater channels.

A Sensitivity Analysis of Simulated Infiltration Rates to Uncertain Discretization in the Moisture Content Domain

An unconditionally mass conservative hydrologic model proposed by Talbot and Ogden provides an effective and fast technique for estimating region-scale water infiltration. It discretizes soil moisture content into a proper but uncertain number of hydraulically interacting bins such that each bin represents a collection of pore sizes. To simulate rainfall-infiltration, a two-step alternating process runs until completion; and these two steps are surface water infiltration into bins and redistribution of inter-bin flow. Therefore, a nonlinear dynamical system in time is generated based on different bin front depths. In this study, using rigorous mathematical analysis first reveals that more bins can produce larger infiltration fluxes, and the overall flux variation is nonlinear with respect to the number of bins. It significantly implies that a greater variety of pore sizes produces a larger infiltration rate. An asymptotic analysis shows a finite change in infiltration rates for an infinite number of bins, which maximizes the heterogeneity of pore sizes. A corollary proves that the difference in the predicted infiltration rates using this model can be quantitatively bounded under a specific depth ratio of the deepest to the shallowest bin fronts. The theoretical results are demonstrated using numerical experiments in coarse and fine textured soils. Further studies will extend the analysis to the general selection of a suitable number of bins.

Infiltration characteristics of lateritic vadose zones: Field experiments and modeling

Lateritic vadose zone, mostly found in tropical humid regions, is very complex and hence behaves in a different way than other vadose zones. Until now, little is known about the impact of agricultural practices on the infiltration dynamics of such vadose zones. To this end, the present study was conceived to explore infiltration characteristics of lateritic soil-water zones by conducting 72 double-ring infiltration experiments at multiple sites considering both plot and field scales having paddy-wheat cultivation practices. Furthermore, five infiltration models, two empirical (Kostiakov and Kostiakov-Lewis) and three process-based (Philip Two-Term, Swartzendruber and Brutsaert) were fitted to the site-specific infiltration data and their performance was critically evaluated for modeling infiltration behavior of the lateritic vadose zone. Results indicated that the mean ‘quasi-steady infiltration rate’ of the Experimental Plot measured before paddy cultivation was about 0.23 ± 0.2 mm/min, which decreased by around 80% after paddy cultivation and then increases by more than 200% after wheat cultivation. Similarly, at the field scale, the ‘quasi-steady infiltration rate’ decreases by about 60% after paddy cultivation. The Swartzendruber model followed by the Brutsaert model provided the most reliable estimates of saturated hydraulic conductivity. The Swartzendruber, Brutsaert and Kostiakov-Lewis models showed a greater fitting ability in predicting ‘cumulative infiltration’ under all cultivation practices at both plot and field scales. In contrast, all the five infiltration models predicted infiltration rate with a lower accuracy than the cumulative infiltration. It is concluded that the infiltration characteristics of lateritic vadose zones are greatly influenced by cultivation practices as well as by macro-pores and low-permeability layers present in the soil profile. Given the significant field heterogeneity, future studies should explore the efficacy of other process-based infiltration models [e.g., the Smith and Parlange (1978) model, the Kutílek and Krejča model (1987), and the modified Green-Ampt models] in predicting the infiltration characteristics of lateritic terrains.

Time Compression Approximation Relationship for Infiltration in the Presence of a Shallow Water Table: Evaluating the Role of Péclet Number

AbstractThe time compression (or time condensation) approximation (TCA) is commonly used in conjunction with an infiltration capacity equation for predicting the postponding infiltration rate, or, more generally, infiltration under time‐varying precipitation. In this paper a power function relationship for TCA between infiltration capacity and its time derivative is proposed for infiltration in the presence of a shallow water table. The results show that the exponent (β) in the power function relationship is not a constant but decreases as infiltration proceeds. The change of β indicates that the TCA relationship changes during infiltration and further suggests the necessity of using different TCA relationships for predicting infiltration rate during different stages after ponding. We argue that the change of β is due to the gradual dynamic change of the relative role of gravity and capillarity during infiltration. A Péclet number (Pe) is proposed for measuring the relative effect of gravity and capillarity. In the early times of infiltration when Pe < 1, with the increase of Pe, β decreases roughly from 3.5 to 2 for clay, silty clay loam, and silty loam, and from 3 to 2 for sandy loam and sand; during the longer times when Pe > 1, β has a linear relationship with Pe. The relationship between Pe and β provides an objective approach to select the suitable TCA function during different infiltration stages after ponding.

Evaluation of Kostiakov’s and Philip’s infiltration models on the soil of Dediapada, India

Infiltration models are used in designing and optimizing irrigation projects as they are capable of predicting infiltration rate and accumulated infiltration depth to a reasonable level of accuracy. The objective of this study deals with obtaining the parameters of infiltration models like Kostiakov and Philip, applying these models to the soil of Dediapada and evaluating their performance by comparing it with the observed infiltration. Firstly, the accumulated infiltration and infiltration rates were determined by the field measurement using a double ring infiltrometer. Kostiakov’s and Philip’s infiltration models were then applied to obtain simulated data once its parameters were ascertained. The estimated parameters ‘m’ and ‘n’ for the Kostiakov model were 0.1311 and -0.3092 respectively and the corresponding equations obtained for estimating infiltration rate and accumulated infiltration depth were 0.13t-0.30 and 0.19t0.69. The estimated parameters ‘s’ and ‘k’ for the Philip model were 0.32 and 0.014 respectively and the corresponding equations obtained for estimating infiltration rate and accumulated infiltration depth were 0.16t-0.5+0.014 and 0.32t0.5+0.014t. The coefficient of determination values for evaluating the performance of the model were obtained in excess of 0.95 for both the models. Due to lack of research on the application of infiltration models on the clay loam soil of Dediapada, this study would prove to be useful for estimation of infiltration rate and depth.

Weather correlations to calculate infiltration rates for U. S. commercial building energy models

As building envelope performance improves, a greater percentage of building energy loss will occur through envelope leakage. Although the energy impacts of infiltration on building energy use can be significant, current energy simulation software have limited ability to accurately account for envelope infiltration and the impacts of improved airtightness.This paper extends previous work by the National Institute of Standards and Technology that developed a set of EnergyPlus inputs for modeling infiltration in several commercial reference buildings using Chicago weather. The current work includes cities in seven additional climate zones and uses the updated versions of the prototype commercial building types developed by the Pacific Northwest National Laboratory for the U. S. Department of Energy.Comparisons were made between the predicted infiltration rates using three representations of the commercial building types: PNNL EnergyPlus models, CONTAM models, and EnergyPlus models using the infiltration inputs developed in this paper. The newly developed infiltration inputs in EnergyPlus yielded average annual increases of 3% and 8% in the HVAC electrical and gas use, respectively, over the original infiltration inputs in the PNNL EnergyPlus models. When analyzing the benefits of building envelope airtightening, greater HVAC energy savings were predicted using the newly developed infiltration inputs in EnergyPlus compared with using the original infiltration inputs. These results indicate that the effects of infiltration on HVAC energy use can be significant and that infiltration can and should be better accounted for in whole-building energy models.

Performance of two prevalent infiltration models for disturbed urban soils

AbstractEstimating infiltration losses is very important for calculating runoff and recharge. However, the accuracy of contemporary infiltration models for disturbed urban soils may not be adequate, potentially compromising calculations based upon them. The objective of this study was to assess the performance of the two most prevalent infiltration models, Horton and Green–Ampt, for applications in urban soils. The data were measured by the US Environmental Protection Agency in a large city for soils with various characteristics of texture, structure, age, compactness, and dryness/wetness. The results indicate both models performed better in predicting infiltration rates for clayey rather than sandy soils, for new rather than old soils, and for wet rather than dry soils. For the clayey soils, both models performed better for the noncompact than compact soils. The opposite was true for sandy soils. Overall, neither infiltration model performed well for most soils, with the sole exception of the new clayey, wet, noncompact soils. The generally poor performance of the models in disturbed soils will likely increase uncertainty in model predictions. This study demonstrates the need to develop improved, more robust infiltration algorithms applicable to urban soils and various kinds of urban development that are based on carefully measured field experimental data.

Modeling gypsifereous soil infiltration rate under different sprinkler application rates and successive irrigation events

The marked reduction in infiltration rate caused by formation of a soil seal due to water droplets impact on gypsifereous soil surface is a well-known phenomenon, but is rarely considered in infiltration models, especially under center-pivot irrigation systems. A simple empirical model based on the Kostiakov equation was developed. The model was applied to center-pivot irrigation system for soils with different gypsum contents [60.0 (G1), 137.6 (G2), 275.2 (G3), 314.2 (G4), and 486.0 (G5) gkg−1] under two sprinkler application rates (84.6mmh−1 for R3000 red-plate sprinkler and 47.3mmh−1 for R3000 orange-plate sprinkler) and eight successive irrigation events. The impacts of sprinkler droplets kinetic energy as well as specific power on soil surface sealing were evaluated also. A good agreement (P<0.05) between the measured and predicted infiltration rate values was obtained.

Estimation of air infiltration for Australian housing energy analysis

This study presents an integrated air flow model for AccuRate, a benchmark housing energy star-rating tool widely used in Australia. The model was developed by implementing a network multi-zonal infiltration model into an existing large opening model. Case studies using 10 houses in Melbourne, Australia, showed that on average the predicted infiltration rates are within 12.6% of the blower-door test results with a standard deviation of 1.6%. The model study also showed that on average the infiltration rates of the 10 houses could be reduced around 52% by sealing major air leakage pathways, which can result in an average of 7% reduction in the annual space heating and cooling loads for the 10 houses. It was also found that an annual hourly energy performance simulation using the new air flow model requires around 15 min in comparison with around 1 min using the existing simple infiltration model in the AccuRate engine.

Infiltration of PM2.5 into Homes in Toronto, Canada: Can Commonly Available Housing Characteristics Be Used to Improve Exposure Estimates?

ISEE-0803 Background and Objective: Epidemiological studies of outdoor air pollution continue to be impacted by errors in estimating personal exposure. Differences in the infiltration of outdoor pollution between homes and over time contribute to exposure errors, but very few epidemiologic studies have considered infiltration because it is not feasible to measure in large numbers of homes and published literature on modeling is still scarce. This study sought to estimate infiltration efficiencies of PM2.5 in detached residential homes in Toronto and identify housing characteristics that could be used to predict infiltration efficiencies. Methods: Fine particulate matter was measured continuously indoors and outdoors for 5-days at 60 detached homes in Toronto, Canada, July through November 2006 and July 2007 using a Dust-Trak. After censoring indoor sources, an average infiltration rate for each home was estimated using a recursive mass balance model with PM2.5 hourly averages. Participating households completed questionnaires on home characteristics and house assessment values were obtained from the Municipal Property Assessment Corporation of Ontario. These variables were offered into linear regression models as predictors of infiltration efficiency. Results: After removal of incomplete and invalid data, 30 homes (50%) remained for inclusion in analyses. Average infiltration rate was 64% (standard dev = 22%); it was higher in the non-heating season (67% ± 23%, n = 21) than in the heating season (56% ± 15%, n = 9), though the difference was not significant. Predictors of higher infiltration were older homes (R2 = 16%) and higher air exchange rate (R2 = 17%) (P < 0.05). In the non-heating season, central air conditioning use also predicted lower infiltration (R2 = 10%, P < 0.05). Other housing characteristics, including house assessment value, were not significantly associated with infiltration rates. Conclusion: Although it remains challenging to predict infiltration rates for individual homes, some easily attainable housing characteristics may allow for the prediction of infiltration in future epidemiological studies of air pollution.