Considering the growing use of permeable pavements, the prediction of runoff passing through this pavement model is of considerable importance. The prediction of rainfall-runoff relationships can be a challenge because of several factors including data uncertainty, non-linear relationships, and high temporal and spatial variability. To deal with these challenges, intelligent algorithms are often used to predict such complex phenomena. In this research, runoff control parameters were investigated in two types of permeable pavements (permeable interlocking concrete pavement (PICP) and high strength clogging resistant permeable pavement (CRP)) using support vector machine (SVM), support vector machine-bat (SVM-BA) and support vector machine-grasshopper (SVM-GOA). Variables used in the models included percentage of coverage by permeable pavement (A), rainfall intensity (I), slope (S), and pavement type coefficient (K) as input data, and runoff coefficient (C), time to runoff (Tr), and peak discharge (Qp) as output data. In this research, from the total of 108 data extracted from the experimental results, 86 data were used in the training period, and 22 data were used in the test period. The results of the test period show that the SVM-BA model has the best performance with values of MAE = 0.010 in predicting C, MAE = 1.330 min in predicting Tr, and MAE = 0.029 lit/min in predicting Qp. The SVM-GOA model is ranked second with values of MAE = 0.051 in predicting C, MAE = 3.285 min in predicting Tr, and MAE = 0.097 lit/min in predicting Qp. Also, the SVM model is ranked third with values of MAE = 0.063 in predicting C, MAE = 4.470 min in predicting Tr, and MAE = 0.121 lit/min in predicting Qp. In summary, the SVM-BA algorithm showed the best performance and the SVM algorithm showed the weakest performance in predicting runoff characteristics in permeable pavements.

Permeable pavement is a technology that allows rainwater to infiltrate into the pavement. Permeable pavements not only help reduce surface runoff by allowing rainwater to infiltrate into the pavement, but also improve water quality with the filter layer that removes particulate matter pollutants. This study evaluated the particulate matter removal efficiency of bottom ash-sand mixtures as filter layers for removing fine (≤10 μm) or ultrafine (≤2.5 μm) particulate matter in the laboratory. Five filter media were tested: silica sand, bottom ash, and bottom ash-sand mixtures with 30:70, 50:50, and 70:30 ratios. The mixed filters exhibited more consistent and stable particulate matter removal efficiency over time than either the uniform sand or bottom ash filter. The 50:50 bottom ash-sand mixture demonstrated removal rates of 58.05% for 1.8 μm particles, 93.92% for 10 μm particles, and 92.45% for 60 μm particles. These findings highlight the potential of bottom ash-sand mixtures as effective filter media for removing PM10 road dust, although field validation with actual pavement systems is necessary.

Since stormwater conveys a variety of contaminants into water bodies, green infrastructure (GI) is increasingly being adopted as an on-site treatment solution in addition to controlling peak flows. The purpose of this study was to identify differences in microbial water quality of stormwater in watersheds retrofitted with GI vs. those without GI. Considering stormwater is recently recognized as a contributor to the antibiotic resistance (AR) threat, another goal of this study was to characterize changes in the microbiome and collection of AR genes (resistome) of urban stormwater with season, rainfall characteristics, and fecal contamination. MinION long-read sequencing was used to analyze stormwater microbiome and resistome from watersheds with and without GI in Columbus, Ohio, United States, over 18 months. We characterized fecal contamination in stormwater via culturing Escherichia coli and with molecular microbial source tracking (MST) to identify sources of fecal contamination. Overall, season and storm event (rainfall) characteristics had the strongest relationships with changes in the stormwater microbiome and resistome. We found no significant differences in microbial water quality or the microbiome of stormwater in watersheds with and without GI implemented. However, there were differences between the communities of microorganisms hosting antibiotic resistance genes (ARGs) in stormwater from watersheds with and without GI, indicating the potential sensitivity of AR bacteria to treatment. Stormwater was contaminated with high concentrations of human-associated fecal bacterial genes, and the ARG host bacterial community had considerable similarities to human feces/wastewater. We also identified 15 potential pathogens hosting ARGs in these stormwater resistome, including vancomycin-resistant Enterococcus faecium (VRE) and multidrug-resistant Pseudomonas aeruginosa. In summary, urban stormwater is highly contaminated and has a great potential to spread AR and microbial hazards to nearby environments. This study presents the most comprehensive analysis of stormwater microbiome and resistome to date, which is crucial to understanding the potential microbial risk from this matrix. This information can be used to guide future public health policy, stormwater reuse programs, and urban runoff treatment initiatives.

The increasing prevalence of microplastics (MP) in urban environments has raised concerns over their negative effects on ecosystems and human health. Stormwater runoff, and road dust and sediment, act as major vectors of these pollutants into natural water bodies. Sustainable urban drainage systems, such as permeable pavements, are considered as potential tools to retain particulate pollutants. This research evaluates at laboratory scale the efficiency of permeable interlocking concrete pavements (PICP) and porous concrete pavements (PCP) for controlling microplastics, including tire wear particles (TWP) which constitute a large fraction of microplastics in urban environments, simulating surface pollution accumulation and Mediterranean rainfall conditions. Microplastic levels in road dust and sediments and stormwater runoff inputs were 4762 ± 974 MP/kg (dry weight) and 23.90 ± 17.40 MP/L. In infiltrated effluents, microplastic levels ranged from 2.20 ± 0.61 to 5.17 ± 1.05 MP/L; while tire wear particle levels ranged between 0.28 ± 0.28 and 3.30 ± 0.89 TWP/L. Distribution of microplastics within the layers of PICP and PCP were also studied and quantified. Microplastics tend to accumulate on the pavements surface and in geotextile layers, allowing microplastic retention efficiencies from 89 % to 99.6 %. Small sized (< 0.1 mm) fragment shaped microplastics are the most common in effluent samples. The results indicate that permeable pavements are a powerful tool to capture microplastics and tire wear particles, especially by surface and geotextile layers. The study aims to shed light on the complex mobilisation mechanisms of microplastics, providing valuable insights for addressing the growing environmental concern of microplastic pollution in urban areas.

Permeable pavement is superior in functions such as reducing noise, improving traffic safety, and protecting urban water environment. However, contaminants on the pavement due to spillage of transported goods, deposits from vehicle wear and tear, and natural settlement can cause the risk of blockage when these contaminants enter the interior of the permeable pavement. Timely maintenance of permeable performance can effectively solve the degradation of environmental performance of permeable pavement caused by clogging. Consequently, exploring permeable pavement clogging patterns can support determining the timing of maintenance. In this paper, simulation clogging material gradations were formulated based on actual road clogging conditions. According to the self-developed permeable pavement clogging simulator, the clogging behavior of permeable pavement was comprehensively explored, taking external conditions, mix proportion, structural combination, and long-term clogging conditions into consideration. Moreover, the effect of external conditions on the clogging pattern was simulated by varying the rainfall intensity and clogging particle size. Furthermore, the effects of gradation, void rate, nominal maximum particle size, and the overwater section on the clogging resistance were investigated. The clogging-sensitive particle size under different structures was determined. It is demonstrated that the water head height is the crucial factor on the clogging behavior. Greater rainfall intensity and water head height lead to more severe clogging. The increase of nominal maximum aggregate size is beneficial to the anti-clogging ability of permeable pavement. Also, the clogging material with small particle sizes is more likely to cause structural clogging. Finally, the evaluation index of clogging level was put forward, which divides the clogging level of permeable pavement into mild, moderate, and severe. The research can support the rationalization, intelligence, and convenience of permeable pavement maintenance timing decisions. Meanwhile, there is key significance to the promotion application and performance maintenance.

Permeable pavements help reduce surface temperatures and have been widely implemented in urban areas. This study utilized an in-use permeable pavement sidewalk in front of a mass rapid transit station in the Taipei city center of Taiwan to determine the actual pavement surface temperature performance. A neighboring asphalt road and impervious pavement were also monitored. With a full year of continuous monitoring, the results showed that the temperature of permeable pavement was 3.7 °C lower than that of impervious pavement and 4.5 °C lower than that of asphalt pavement in the hot season. The frequent rainfall in spring resulted in the smallest temperature differences between the different pavement types. The cooling effects of permeable pavement differed at the different air temperatures. At air temperatures lower than 15 °C, the differences among pavement surface temperatures were noticeable. However, when the air temperature was higher than 35 °C, the surface temperature of permeable pavement was not different from that of impervious pavement and was greater than 55 °C. Field observations were carried out to determine the effects on the apparent temperature and the future surface temperature of climate change scenarios. The results showed that permeable pavement could reduce the average apparent temperature to near the air temperature, and asphalt pavement could increase the apparent temperature by 1.2 °C, assuming that the pavement temperature completely affects the air temperature. With the good prediction ability of the machine learning approach and 15 environmental factors, the preliminary prediction showed the projected surface temperature change in Taipei city in 2033. In the worst-case scenario, the average impervious pavement temperature is as high as 39.12 °C, whereas the average permeable pavement temperature is 32.50 °C.

The dataset currently available comprises data on the removal rates of heavy metals (Ba, Se, Cr, Fe, Cd, As, and Co) through the incorporation of seashells and palm oil kernel shells into pervious concrete for stormwater treatment. Stormwater runoff was collected from commercial areas in Taman University, Skudai, Johor, Malaysia. The stormwater samples underwent filtration and were preserved in high-density polyethylene (HDPE) bottles at a temperature of 4 °C for use as incoming water. The outgoing water, referred to as effluent, was obtained from tests performed on pervious concrete samples after a curing period of 28 days. The pervious concrete mixes were created with a water-to-binder ratio (w/b ratio) of 32% and a sand ratio of 10%. Three different levels of palm oil kernel shell and seashell content were used as coarse aggregate replacements: 0%, 25%, and 50%. Two single-size group were considered for both palm oil kernel shell and seashell: (6.3-9.5 mm) and (4.75-6.3 mm). Heavy metal analyses were conducted on the influent and effluent using a PerkinElmer ELAN 6100 Series Inductively Coupled Plasma- Mass Spectrometer (ICP-MS). The available datasets consist of both raw and analyzed data.

Permeable pavement is a highly effective technology in Low-Impact Development (LID) for managing stormwater runoff, which helps mitigate environmental impacts. Filters are essential components of permeable pavement systems as they prevent permeability reduction, remove pollutants, and enhance the system\'s overall efficiency. This research paper focuses on exploring the influence of three factors, including total suspended solids (TSS) particle size, TSS concentration, and hydraulic gradient, on the permeability degradation and TSS removal efficiency of sand filters. A series of tests were conducted using different values of these factors. The results demonstrate that these factors have an influence on permeability degradation and TSS removal efficiency (TRE). A larger TSS particle size results in higher permeability degradation and TRE than a smaller particle size. Higher TSS concentrations lead to higher permeability degradation and lower TRE. Additionally, smaller hydraulic gradients are associated with higher permeability degradation and TRE. However, the influence of TSS concentration and hydraulic gradient seems less significant than that of TSS particle size for the values of the factors considered in the tests. In summary, this study provides valuable insights into the effectiveness of sand filters in permeable pavement and identifies the main factors that influence permeability degradation and TRE.

In 2009, a permeable pavement research and demonstration site was constructed at the Edison Environmental Center, Edison, NJ. Infiltration testing of three original permeable parking rows through August 2012 indicated that clogging occurred along the upgradient edge of these pavements from runoff that drained from adjacent impermeable driving lanes. A subsequent infiltration testing data collection effort from April 2017 through March 2020 focused on permeable interlocking concrete pavers (PICP) that replaces one of the original permeable surfaces. While the original infiltration study through 2012 used random locations throughout the permeable parking rows, the newer study targeted upgradient edge to identify where clogging would occur. Testing locations along the upgradient edge were selected based on a high-resolution survey (HRS) of the parking lot performed in December 2014. The HRS identified three low spots along the upgradient edge that eventually clogged in the new PICP infiltration study. The HRS may assist with maintenance routines. The newer study also supports the conclusion of the earlier study with regards to truncating the infiltration testing method, particularly for maintenance assessments.

In this study, seven roads and parking lots were sampled by a road surface cleaning truck and approximately 100 kg of particulate material was collected per site. Thereafter, the samples were analysed for microplastics, including tyre wear particles. The analyses revealed that tyre wear constituted 0.09 % of the dry mass of the samples on average. Other plastic types were also identified in the samples, but at on average 49 times lower concentrations compared to tyre wear particles. Although the roads and parking lots were used for residential, industrial, and commercial purposes, no correlation between land use and the total concentrations of microplastics was identified. Of microplastics other than tyre wear particles, polypropylene constituted an important fraction in all samples, whereas other polymers were present at various degrees. The contents of heavy metals, sulphur, and total organic carbon were also measured in the samples, but no correlation between them and microplastics was determined. A back-of-the-envelope estimation indicated that the tyre wear material retained by permeable pavements constituted a non-negligible fraction of the total mass of microplastics released on roads and parking lots. Therefore, permeable pavements can serve as a tool for the management of this pollutant.