A simplified method for estimating heavy metal pollution loads in urban road storm runoff

Abstract

The invention discloses a simplified method for estimating the heavy metal pollution load of urban road storm runoff; the method comprises the following steps: selecting a corresponding sampling process according to the dry season and the rainy season; the steps in the dry season are: A2, sampling dry season road sediments; A3, analyzing the particle size composition characteristics of the sampled material; A4, conducting a leaching test on dry season road sediments; A5, obtaining the characterization of dry season road sediments; A6, evaluating the heavy metal load of dry season road sediments under dry weather; A7, constructing a simplified scour equation; the steps in the rainy season are: B2, sampling rainy season road runoff; B3, calculating instantaneous flow using the Manning equation; B4, event average concentration analysis; B5, heavy metal analysis of dissolved phase and particle phase of rainy season road runoff; B6, instantaneous concentration linear interpolation calculation; finally, constructing an estimation method. The invention can be used as an important basis for implementing heavy metal pollution control of road storm runoff during rainfall.

Description

Technical Field

[0001] The invention relates to the technical field of urban sewage treatment, and in particular to a simplified method for estimating heavy metal pollution load of urban road storm runoff. Background Art

[0002] In the prior art, stormwater runoff from urban catchments is widely considered to be a major source of environmental pollutants, and many studies have shown that stormwater-related pollutants enter surrounding receiving water bodies and accumulate, with serious adverse effects on the ecosystem health of the aquatic environment. In highly urbanized catchments, pavement can typically account for 22% of the total catchment area, contributing 26% of the total runoff, and a corresponding contribution of 19-40% to the heavy metal load (such as copper, lead, and zinc). In terms of runoff generation and pollutant output, road runoff is second only to residential runoff. Therefore, it should be given priority as a non-point source of urban pollution when developing any comprehensive stormwater management plan.

[0003] To obtain such data, it is relatively simple to conduct a field survey of the relative stormwater pollutant loadings of different roads to construct a road pollutant export model.

[0004] However, in practice it is currently difficult to directly compare pollutant loads on different road sections due to the strong influence of physical and structural factors such as braking status, road slope, proximity to intersections, traffic lights, curves and side entrances and exits without any obvious systematic relationship between these parameters.

[0005] Spatial and temporal variability in meteorological conditions, such as wind strength and direction, rainfall intensity and duration, also affect the accumulation of particle pollutants and the movement of accumulated materials. These processes interact with the direction and orientation of the considered road segment.

[0006] Moreover, the factors that influence the spatial and temporal variations of road runoff composition and flow are numerous and highly complex, in addition to the known difficulties in obtaining representative time series and sampling data for heavy rainfall.

[0007] Furthermore, direct comparisons between different road sites are highly unreliable and meaningful results cannot be expected from this approach.

[0008] The study showed that a “pollutant yield rating curve” can be constructed for a given location based on cumulative data on pollutant loads emitted at each event, and that these rating curves can be compared between locations to provide a clear and understandable picture of road pollutant output for different types and situations.

[0009] Heavy metals in road runoff have been extensively studied in the prior art, but the results of such studies are usually reported in the form of a single parameter, namely event mean concentrations (EMCs), which is broadly defined as the total pollutant load divided by the total discharge of the runoff event.

[0010] Therefore, the use of a single value to describe the complex relationship between instantaneous flow and pollutant concentrations, and the variation in these behaviors with runoff event size, means that there is little value in comparing EMCs data from different locations for the purpose of determining relative loading rates. Initial flush, a period of elevated pollutant concentrations at the beginning of an event that results in more pollutants being transported in the first half of an event than in the second half, is a key characteristic of roadway runoff that makes EMCs unreliable as a method of calculating pollutant loadings for all total discharges.

[0011] Therefore, how to extract useful information from the comparison of runoff studies on different roads and avoid the many confounding factors mentioned above has become a technical problem that technicians in this field need to solve urgently. Summary of the invention

[0012] In view of the above-mentioned defects of the prior art, the present invention provides a simplified method for estimating the heavy metal pollution load of urban road storm runoff, the purpose of which is to more scientifically and conveniently extract useful information from comparative studies on runoff from different roads, so as to serve as an important basis for implementing the control of heavy metal pollution in road storm runoff during rainfall.

[0013] To achieve the above object, the present invention discloses a simplified method for estimating the heavy metal pollution load of urban road storm runoff; the method comprises the following steps:

[0014] Step 1: Select the corresponding sampling process according to the dry season and the rainy season. In the dry season, execute step A2, and in the rainy season, execute step B2;

[0015] Step A2, sampling dry season road sediment (RDS);

[0016] Step A3, analyzing the particle size composition characteristics of the sample;

[0017] Step A4, performing a leaching test on dry season road sediment (RDS);

[0018] Step A5, obtaining a representation of the dry season road sediment (RDS) load;

[0019] Step A6, evaluating the heavy metal load of the road sediment under dry weather;

[0020] Step A7: Simplify the flushing equation and then proceed to step 8;

[0021] Step B2, sampling the road runoff in the rainy season;

[0022] Step B3, using the Manning equation to calculate the instantaneous flow;

[0023] Step B4, event mean concentration (EMC) analysis;

[0024] Step B5, analysis of heavy metals in dissolved and particulate phases of road runoff during the rainy season;

[0025] Step B6, instantaneous concentration linear interpolation calculation;

[0026] Step 8: Construct the estimation method.

[0027] Preferably, in step A2, the sampling surface area for sampling dry season road sediment (RDS) is 0.50m 2 , i.e. 707mm×707mm, the size of the sampling surface area is ensured by placing an acrylic frame on the surface;

[0028] Using a vacuum cleaner of 1.5 kW or more in series with a plastic fiber brush, retaining particles larger than 1 μm as the sample;

[0029] Sampling of dry season road sediment (RDS) consists of two stages;

[0030] The first stage involves tertiary sediment extraction directly from the road surface;

[0031] The second stage is to sweep the same surface with a plastic fiber brush;

[0032] A light sweep was applied to the sampled road surface, and attempts were made to apply the same force while sweeping throughout the sampling process;

[0033] The total amount of sediment on the road surface was calculated as the sum of the RDS collected in the two stages.

[0034] Preferably, in step A3, the particle size distribution of RDS is determined using the ISO-11277 method;

[0035] The mesh size for particle size distribution (PSD) analysis in road sediment is between 63 μm and 2800 μm, specifically including particles that can pass through a 63 μm mesh, a 63 μm to 125 μm mesh, a 125 μm to 250 μm mesh, a 250 μm to 500 μm mesh, a 500 μm to 1000 μm mesh, a 1000 μm to 2000 μm mesh, and a 2000 μm to 2800 μm mesh;

[0036] Statistical analysis was performed using the Kolmogorov-Smirnov test to assess the normality of the particle size distribution (PSD) data set in road sediments and by logarithmically transforming these data to assess the adjustment for this probability distribution;

[0037] Linear regression analysis was used to establish a model between particle size and the mass percentage of RDS in each particle size fraction.

[0038] Preferably, in step A4, deionized water and RDS are mixed at an L / S ratio of 10 L / kg dry matter;

[0039] Mechanical shaking at 10 rpm for 24 h in the dark at room temperature;

[0040] After completion, the supernatant was filtered using a polytetrafluoroethylene (PTFE) membrane filter to obtain the RDS permeate;

[0041] In the leaching test, the potential conditions of turbulent runoff on the RDS were simulated, and the determination of heavy metals in the RDS and leachate was carried out according to the HJ / T 350-2007 and HJ 700-2014 methods.

[0042] Preferably, the constructed simplified scour equation is used to calculate the heavy metal pollution load of storm runoff from RDS, and the equation expression is:

[0043]

[0044] Where TLW is the potential load of HM washed off by runoff from the RDS, expressed as a percentage;

[0045] LW <250,i is the percentage of RDS susceptible to runoff migration at 250 μm;

[0046] ML <250,i is the percentage of heavy metals related to RDS at 250 μm;

[0047] LE<250 is the leaching rate of RDS-associated heavy metals at 250 μm;

[0048] LE ≥250 It indicates the percentage of heavy metal leaching when RDS is greater than or equal to 250 μm;

[0049] Polynomial (1-LW <250,i / 100) represents the proportion of RDS below 250 μm that is not lost by runoff.

[0050] Preferably, in step B2 and step B3, an automatic sampler is installed at each sampling location to sample the runoff in the outlet pipe of the corresponding drainage pit;

[0051] monitoring the water level in each of said outlet pipes using a pressure sensor and calculating the instantaneous flow rate using the Manning equation and intermittent field verification using a Doppler velocity sensor integrated into the depth sensor;

[0052] When flow is detected, samples are taken at predetermined intervals, starting with 6-minute intervals and doubling every four intervals.

[0053] Preferably, before step B4, the sample is collected as soon as possible within 24 to 48 hours after the road runoff event occurs; after returning to the laboratory, the sample is pre-processed as follows:

[0054] Stir the sample and extract a homogenous portion for other analyses;

[0055] The remaining sample in the collection container was then acidified with 0.5% HNO3 and stored at room temperature for at least one week before in-house analysis.

[0056] More preferably, after vigorously shaking the pretreated sample, two 10 mL dissolved phase subsamples are taken directly from the bottle and filtered to 0.45 μm;

[0057] The remaining sample was prefiltered through a 250 μm nylon mesh, standardized according to maximum particle size, and then passed through a preweighed and dried 0.45 μm membrane filter using a vacuum filtration manifold, unsieved nitrocellulose;

[0058] After completion, the filtered filter paper was dried in an oven at 60°C;

[0059] After completion, it was digested in batches using aqua regia and used as the particle phase;

[0060] Finally, the digested sample was made up to 15 mL with ultrapure water, and 10 mL of 0.45 μm filtered subsample was taken out for analysis; all subsamples were refrigerated before analysis.

[0061] More preferably, the sample is analyzed for a range of metals using Varian axial inductively coupled plasma atomic emission spectroscopy (ICP-AES);

[0062] The total concentration was calculated as the sum of the concentrations in the dissolved phase and the particulate phase, i.e., after digestion.

[0063] Preferably, in step B7, the time series of flow rate and pollutant concentration are upsampled from variable interval data and sampling time to 1 minute interval;

[0064] The total load for each event was calculated by linear interpolation of the instantaneous concentrations determined with a 1-min resolution and by logarithmic calculations of the given pollutant generation rate curves, which were normalized for rainfall depth and road area.

[0065] Beneficial effects of the present invention:

[0066] The invention can be used as an important tool for effectively and quantitatively evaluating the impact of heavy metal pollution in road storm runoff on surrounding soil and water bodies.

[0067] The present invention can analyze the potential heavy metal pollution loads of RDS runoff washout accounted for by solid and dissolved components, respectively, and thus can be used as an important tool for the control of heavy metal pollution in road storm runoff during rainfall.

[0068] The application of the present invention can eliminate as many confounding parameters as possible and can better illustrate the descriptive power of the pollutant yield rating curve method. More importantly, the method only requires sampling of pavement runoff without disturbances that may affect the initial flushing characteristics of runoff discharge, such as input from non-pavement or runoff to permeable boundaries.

[0069] The present invention eliminates the need for conventional continuous runoff sampling and testing, and in particular allows the use of suspended solids as a substitute for these metals to analyze runoff samples, thereby greatly reducing the cost of analysis and saving a large amount of manpower and monitoring costs.

[0070] The present invention can be combined with sampling activities such as suspended matter sampling or monitoring, periodic atmospheric sampling to determine metal concentrations, and current pollutant yield assessment curve analysis, and can be considered as an inexpensive, simple and reliable method to establish a broad basin-wide trace metal loading database by road type or runoff discharge point for remediation planning purposes.

[0071] The present invention can be used as a management tool for agencies responsible for controlling diffuse pollution in urban areas. For example, optimization of sweeping frequency and timing in dry weather and optimization of permeable / porous pavement design, infiltration / percolation trenches and basins, and detention / retention ponds in rainy weather should guide the design of contaminated runoff control to eliminate fine-grained components (e.g., <250 μm) in RDS. Therefore, the proposed method can be used to further study the impact of rainfall events on the HM load of RDS runoff.

[0072] The concept, specific structure and technical effects of the present invention will be further described below in conjunction with the accompanying drawings to fully understand the purpose, characteristics and effects of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 A flow chart showing an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0074] Example 1

[0075] like Figure 1As shown in Figure 1, a simplified method for estimating heavy metal pollution loads in urban road storm runoff includes the following steps:

[0076] Step 1: Select the corresponding sampling process according to the dry season and the rainy season. In the dry season, execute step A2, and in the rainy season, execute step B2;

[0077] Step A2, sampling dry season road sediment (RDS);

[0078] Step A3, analyzing the particle size composition characteristics of the sample;

[0079] Step A4, performing a leaching test on dry season road sediment (RDS);

[0080] Step A5, obtaining a representation of the dry season road sediment (RDS) load;

[0081] Step A6, evaluating the heavy metal load of the road sediment under dry weather;

[0082] Step A7: Simplify the flushing equation and then proceed to step 8;

[0083] Step B2, sampling the road runoff in the rainy season;

[0084] Step B3, using the Manning equation to calculate the instantaneous flow;

[0085] Step B4, event mean concentration (EMC) analysis;

[0086] Step B5, analysis of heavy metals in dissolved and particulate phases of road runoff during the rainy season;

[0087] Step B6, instantaneous concentration linear interpolation calculation;

[0088] Step 8: Construct the estimation method.

[0089] In practical application, the present invention comprises the following three stages for the dry season:

[0090] (1) Characterization of RDS load;

[0091] (2) Assessment of heavy metal loads under drought conditions;

[0092] (3) Application of the scour equation.

[0093] At the same time, combined with the use of pollutant yield rating curves, the heavy metal loads in storm runoff from two roads with significantly different traffic volumes were estimated and compared to eliminate as many confounding parameters as possible and better illustrate the descriptive power of the pollutant yield rating curve method.

[0094] When characterizing road sediment (RDS) runoff loads, it is necessary to standardize sampling and analyze the particle size composition characteristics of the samples.

[0095] The present invention focuses on suspended solids and heavy metals copper, lead and zinc as representative examples of pollutants of concern in urban water bodies. The method first characterizes the road sediment (RDS) load washoff; and evaluates the heavy metal load under dry weather, then applies the washoff equation to calculate heavy metal transport (solid component) and heavy metal leaching (dissolved component), and finally uses the pollutant yield grade curve to estimate the heavy metal pollution of road storm runoff.

[0096] Compared with the prior art, the present invention is more scientific and simpler, and can be used as an important basis for the treatment of heavy metal pollution in road storm runoff during rainfall.

[0097] In some embodiments, in step A2, the sampling area of ​​the dry season road sediment (RDS) is 0.50 m 2 , i.e. 707mm×707mm, the size of the sampling surface area is ensured by placing an acrylic frame on the surface;

[0098] Use a vacuum cleaner of 1.5kW or more in series with a plastic fiber brush to retain particles larger than 1μm as samples;

[0099] Sampling of dry season road sediment (RDS) consists of two stages;

[0100] The first stage involves tertiary sediment extraction directly from the road surface;

[0101] The second stage is to sweep the same surface with a plastic fiber brush;

[0102] A slight sweep was applied to the sampling surface, and attempts were made to apply the same force throughout the sweep;

[0103] The total amount of sediment on the road surface was calculated as the sum of the RDS collected in the two stages.

[0104] In practice, the two-stage sampling approach helps to avoid the loss of the fine-sized fraction of the RDS associated with suspension, such as that caused by the initial sweep of the surface with a brush, by enabling the sediment adhering to the surface to be sucked away by the above-mentioned technical means.

[0105] Leaching tests of heavy metals in RDS were performed to simulate turbulent runoff conditions during roadside stormwater discharge.

[0106] In certain embodiments, in step A3, the particle size distribution of RDS is determined using the ISO-11277 method;

[0107] The mesh size for particle size distribution (PSD) analysis in road sediment is between 63 μm and 2800 μm, specifically including particles that can pass through a 63 μm mesh, a 63 μm to 125 μm mesh, a 125 μm to 250 μm mesh, a 250 μm to 500 μm mesh, a 500 μm to 1000 μm mesh, a 1000 μm to 2000 μm mesh, and a 2000 μm to 2800 μm mesh;

[0108] Statistical analysis was performed using the Kolmogorov-Smirnov test to assess the normality of the particle size distribution (PSD) data set in road sediments and by logarithmically transforming these data to assess the adjustment for this probability distribution;

[0109] Linear regression analysis was used to establish a model between particle size and the mass percentage of RDS in each particle size fraction.

[0110] In certain embodiments, in step A4, deionized water is mixed with RDS at an L / S ratio of 10 L / kg dry matter;

[0111] Mechanical shaking at 10 rpm for 24 h in the dark at room temperature;

[0112] After completion, the supernatant was filtered using a polytetrafluoroethylene (PTFE) membrane filter to obtain the RDS permeate;

[0113] In the leaching test, the potential conditions of turbulent runoff on the RDS were simulated, and the determination of heavy metals in the RDS and leachate was carried out according to the HJ / T 350-2007 and HJ 700-2014 methods.

[0114] In some embodiments, the constructed simplified washoff equation is used to calculate the heavy metal pollution load of storm runoff from RDS, and the equation expression is:

[0115]

[0116] Where TLW is the potential load of HM washed off by runoff from the RDS, expressed as a percentage;

[0117] LW <250,i is the percentage of RDS susceptible to runoff migration at 250 μm;

[0118] ML <250,i is the percentage of heavy metals related to RDS at 250 μm;

[0119] LE<250 is the leaching rate of RDS-associated heavy metals at 250 μm;

[0120] LE ≥250It indicates the percentage of heavy metal leaching when RDS is greater than or equal to 250 μm;

[0121] Polynomial (1-LW <250,i / 100) represents the proportion of RDS below 250 μm that is not lost by runoff.

[0122] In practical applications, since TLW depends on the fraction (n) of particles with a diameter below 250 μm, the equation established using this simplified method can be used to calculate the potential load of heavy metals produced by road runoff on the RDS.

[0123] In certain embodiments, in step B2 and step B3, an automatic sampler is installed at each sampling location to sample runoff in the outlet pipe of the corresponding drainage pit;

[0124] The water level in each outlet pipe was monitored using a pressure sensor and the instantaneous flow rate was calculated using the Manning equation, with intermittent field verification using a Doppler velocity sensor integrated into the depth sensor;

[0125] When flow is detected, samples are taken at predetermined intervals, starting with 6-minute intervals and doubling every four intervals.

[0126] In practical application, the Manning equation has appropriate slope parameters, roughness and geometry; the slope parameter is 4% in both cases, based on the road slope; the roughness n = 0.015, the wall concrete channel; and the geometry is a pipe with a diameter of 0.4m.

[0127] And because the probes are susceptible to contamination from sediment or trash buildup, velocity measurements are intermittent.

[0128] In some embodiments, before step B4, the sample is collected as soon as possible within 24 to 48 hours after the road runoff event occurs; after returning to the laboratory, the sample is pre-processed as follows:

[0129] Stir the sample and extract a homogenous portion for other analyses;

[0130] The remaining sample in the collection container was then acidified with 0.5% HNO3 and stored at room temperature for at least one week before in-house analysis.

[0131] In certain embodiments, after the pretreated sample is vigorously shaken, two 10 mL dissolved phase subsamples are taken directly from the bottle and filtered to 0.45 μm;

[0132] The remaining sample was prefiltered through a 250 μm nylon mesh, standardized according to maximum particle size, and then passed through a preweighed and dried 0.45 μm membrane filter using a vacuum filtration manifold, unsieved nitrocellulose;

[0133] After completion, the filtered filter paper was dried in an oven at 60°C;

[0134] After completion, it was digested in batches using aqua regia and used as the particle phase;

[0135] Finally, the digested sample was made up to 15 mL with ultrapure water, and 10 mL of 0.45 μm filtered subsample was taken out for analysis; all subsamples were refrigerated before analysis.

[0136] In certain embodiments, samples are analyzed for a range of metals using Varian axial inductively coupled plasma atomic emission spectroscopy (ICP-AES);

[0137] The total concentration was calculated as the sum of the concentrations in the dissolved phase and the particulate phase, i.e., after digestion.

[0138] In some embodiments, in step B7, the time series of flow and pollutant concentration are upsampled from variable interval data and sampling times to 1 minute intervals;

[0139] The total load for each event was calculated by linear interpolation of the instantaneous concentrations determined with a 1-min resolution and by logarithmic calculations of the given pollutant generation rate curves, which were normalized for rainfall depth and road area.

[0140] Example 2

[0141] Taking a city's expressway and ordinary roads as examples, the pollutant yield grade curve is used to compare the heavy metal loads in the rainwater runoff of the city's main roads and secondary roads. The specific steps are as follows:

[0142] 1. Selection of road sites

[0143] Road runoff was sampled from roadside drains on a major high-volume road (WZDD) with an annual average daily traffic (AADT) of 74,900 vehicles / day and on a typical local street (SJL) with an AADT of 2,800 vehicles / day (measured data) between August 2018 and April 2019. The two sections of road were bounded by a concrete curb, had similar slopes, were close to a road bend, and had similar drainage areas (1,069 m2 for SJL; 1,295 m2 for WZDD). 2) and were 700 m apart. Neither road segment had external (i.e. residential) drainage input to the road drain, and the drainage pits from which sampling was performed were free-flowing with a single large-diameter outlet pipe and no other inputs. Similarity of condition between the road segments was not necessary for application of the present model, but eliminating as many confounding parameters as possible would better illustrate the descriptive power of the pollutant yield rating curve approach. More importantly, it was desirable to sample only the road surface runoff without disturbances that could affect the initial flushing characteristics of the runoff discharge, such as inputs from non-pavement surfaces or runoff to pervious boundaries.

[0144] 2. On-site sampling

[0145] An automatic sampler (Sigma900 MAX, Hach, USA) was installed at each site and runoff was sampled in a concrete outlet pipe adjacent to the drainage pit. The water level in the outlet pipe was monitored using a pressure sensor and the instantaneous flow rate was calculated using the Manning equation with appropriate parameters for slope (4% in both cases, based on the road slope), roughness (n = 0.015, plastered concrete channel) and geometry (0.4 m diameter pipe) and intermittent field validation using a Doppler velocity sensor integrated in the depth sensor. Velocity measurements were intermittent as the probe was susceptible to contamination by sediment or trash accumulation. When flow was detected, samples were taken at predetermined time intervals, starting with a 6-min interval and doubling every four intervals.

[0146] 3. Sample analysis

[0147] The samples were collected in 1 L polyethylene bottles and previously heated in 10% HNO 3 Soak overnight in ultrapure water, rinse twice with ultrapure water, and heat in a clean oven at 60°C until completely dry. Cap in the laboratory. In the field, load the bottles into the autosampler (24 bottles) and open the cap before sealing the dispenser of the device. Collect samples as soon as possible within 24 hours after the runoff event. Once back in the laboratory, stir the samples and extract a homogenized portion for other analyses. Then use 0.5% HNO 3 Acidify the remaining sample in the collection container and store at room temperature for at least one week. After vigorous shaking, remove two 10 mL aliquots of the dissolved phase (filtered to 0.45 μm) directly from the bottle. The remaining sample is pre-filtered through a 250 μm nylon mesh (standardized for maximum particle size) and then passed through a pre-weighed and dried 0.45 μm membrane filter (unsieved nitrocellulose) using a vacuum filtration manifold. The filter paper with the filter paper is dried in an oven at 60 °C. The sample is then filtered using aqua regia (1:1 HCl:HNO 3) Digest in batches. Make up the digested sample to 15 mL with ultrapure water and take 10 mL of 0.45 μm filtered aliquots for analysis. All aliquots should be refrigerated until analysis.

[0148] A range of metals were analyzed using inductively coupled plasma atomic emission spectroscopy (ICP-AES). Among the results of the metal analysis, copper, lead and zinc were detected in most samples. The total concentration was calculated as the sum of the concentrations in the dissolved phase and the particle phase (after digestion). The precision of the ICP-AES analysis was evaluated by tabulating the relative standard deviation (RSD) of the measured concentrations for each sample (3 replicates). Replicate analyses with RSD values ​​greater than 20% were excluded from further consideration because they clearly exceeded the reliable detection limit of the instrument, and analyses with RSD values ​​in the range of 5-20% were treated with extreme caution in subsequent calculations (i.e., not included in the statistical calculations, but displayed on the graphs). The RSDs of all copper and zinc analyses and 67% of lead analyses of the digested particle samples were less than 5% (i.e., within the reliable analytical range of the instrument). For the digested dissolved phase samples, 93% of the copper, 97% of the zinc and 21% of the lead analyses had analytical RSDs less than 5%. When the relative standard deviation was 5% or higher, the minimum detection concentrations of copper, lead and zinc were 3.16, 22.43 and 1.21 mg / L, respectively.

[0149] NIST International Reference Material SRM-1648a (Urban Particulate Matter) was used as the procedural and analytical reference material for road runoff samples. The total recoveries of copper, lead, and zinc obtained from this highly contaminated sample by thermally assisted aqua regia digestion were 91.2%, 97.2%, and 89.9%, respectively, with relative standard deviations of 4.5%, 3.6%, and 2.6%, respectively, for 13 reference digestions.

[0150] 4. Data processing

[0151] Of the numerous runoff events sampled at each site over the eight months the sampler was implemented, only seven (WZDD) and eight (SJL) events were sufficiently sampled to allow attempted load calculations, and only four (Queen Street) and six (Parramatta Road) events were considered sufficiently sampled relative to the flow record to provide reliable load estimates. Time series of flow and pollutant concentrations were upsampled to 1-min intervals from variable interval data and sampling times, and total loads for each event were calculated by linear interpolation of the determined instantaneous concentrations at 1-min resolution.

[0152] 5. Main Results

[0153] Events sampled simultaneously at two existing road sites could not be directly related in a meaningful way, even though the distance between the two sites was relatively short, due to differences in runoff depth and hydrograph dynamics associated with differences in meteorological conditions, demonstrating the difficulty of obtaining directly comparable data at multiple sites. However, the loads calculated for events were considered to be sufficiently sampled at the waterline at the appropriate time to provide reliable load estimates, which were well explained by the logarithmic relationships relative to total flow. Even with relatively few data pairs, the explanatory power and significance of these relationships for the two road sites were very high (r 2 >0.92, p<0.01). The fit of the logarithmic relationship is consistent with the regular occurrence of the first flush in the mass / flow relationship.

[0154] Comparisons were made using pollutant yield rating curves derived from densely sampled data. Event loadings for copper, lead, and zinc closely follow the logarithmic relationship of total event emissions because initial washout reliably occurs in the pollutant mass loadings of urban roads. Comparison of the pollutant yield rating curves for the three metals shows that copper and zinc export rates from local streets are comparable to arterial roads, while lead output rates from local streets are much higher, despite a 45-fold difference in traffic volume. The pollutant yield rating curve approach allows problematic environmental data to be presented in a simple but meaningful way with less information loss.

[0155] Example 3

[0156] Take the roads around a steel smelting area, focusing on the downwind direction of the smelting area. Mainly considering that the atmospheric deposition in this area is high, the heavy metal content in dry deposition will also be high, which is conducive to reflecting the application effect of a simplified method for estimating the heavy metal pollution load of urban road storm runoff constructed by this patent. A total of 2 sampling points were selected on the nearby roads.

[0157] 2. RDS sampling

[0158] RDS samples were collected daily on the roadside (within 0.5 m) in dry weather from October 11, 2020 to January 15, 2021. The sampling area was 0.50 m 2(707 mm × 707 mm). The weight of each RDS ranged from 10 to 180 g per square meter. The size of the collection area was ensured by placing an acrylic frame on the surface. For RDS collection, a vacuum cleaner (1.5 kW) was used, in tandem with a plastic fiber brush capable of retaining particles larger than 1 μm. The RDS collection system consisted of two stages. The first stage consisted of suctioning the sediment directly from the pavement (done three times). The second stage consisted of sweeping the same surface with a plastic fiber brush so that the sediment adhering to the surface could be sucked away (done three times). This two-step sampling sequence (vacuuming and sweeping vacuuming) helped to avoid the loss of the fine-sized fraction of the suspension-related RDS, which could be caused by the initial sweeping of the surface with the brush. To prevent the loosening of pavement particles, the sampling surface was gently swept and an attempt was made to apply the same force during the sweeping throughout the sampling period. The total pavement sediment load was calculated as the sum of the RDS collected in both stages. The sampling locations were controlled to prevent duplication and to ensure proximity to previous collection points. On average, 56 RDS samples were collected every 3 days. Ten field tests were conducted on pavement to evaluate the effectiveness of the RDS collection system in dry weather. The test required three consecutive uses of the sampling system to evaluate the remaining RDS load (weight) after each use (characteristics of the selected pavement: asphalt / rough; slope: 1%; collected RDS load: 40 to 55 g).

[0159] 2. Laboratory analysis

[0160] (1) RDS particle size distribution characteristics

[0161] The particle size distribution (PSD) of RDS was determined using the ISO-11277 method (ISO, 2000). The mesh sizes used for PSD analysis ranged from 63 to 2800 μm (b63, 63–125, 125–250, 250–500, 500–1000, 1000–2000, and 2000–2800 μm). Statistical analysis was performed using the Kolmogorov–Smirnov test to assess the normality of the PSD data set. These data were previously log-transformed to assess the adjustment for this probability distribution. Linear regression analysis was used to establish a model between particle size (μm) and the mass percentage of RDS in each size fraction. This analysis was performed to determine the percentiles of RDS particle sizes associated with 10 (d10), 50 (d50), and 90 (d90). The percentile changes before and after each rainfall event (n = 19 rainfall events) were also analyzed. The sensitivity analysis of RDS to runoff-induced washoff considered the particle diameters of all rainfall events at the site during the sampling period. The PSD before and after each rainfall event was used to evaluate the RDS load washoff for all size fractions (the difference in RDS mass before and after each rainfall event). The analysis also included rainfall intensity, ADP, and road slope. Pearson correlation coefficient analysis was used to evaluate the relationship between the first three variables and the RDS load washoff after each rainfall event.

[0162] (2) Leaching test of heavy metals in RDS

[0163] We performed leaching tests on HMs associated with RDS according to the DIN 38414-S4 standard (DIN, 1984). This allowed the simulation of turbulent runoff conditions during storm drainage on roadside surfaces. Deionized water and RDS were mixed in an L / S ratio of 10 L / kg (100 g RDS) dry matter and mechanically shaken (Heidolph, REAX 20 / 4) at 10 rpm for 24 h at room temperature in the dark. The supernatant was filtered using a polytetrafluoroethylene (PTFE) membrane filter (Millipore, XX1004700, pore size 0.45 μm) to obtain the RDS leachate. The collected RDS samples were subjected to 48 leaching analyses (24 for each study area). The leaching tests simulated the potential conditions of turbulent runoff on the RDS; mainly due to the simulated flushing time (t = 24 h), which can range from minutes to hours in a typical storm.

[0164] (3) Determination of heavy metal content in RDS samples and eluents

[0165] After the RDS sample is digested, heavy metal analysis is performed using ICP-MS, and the eluent is directly measured for heavy metal content by ICP-MS after passing through the membrane. The specific method and steps are shown in Example 1.

[0166] 3. Estimation of RDS heavy metal load

[0167] The simplified road storm runoff erosion equation constructed using this patent was used to calculate the potential load of HMs (Pb, Zn, Cu, Cr, Ni, Cd, Fe, Mn, Co and Ba) generated by runoff erosion on the RDS. Figure 1 Results of the potential load of HMs caused by RDS runoff are shown. On average, 31.9.2 and 38.4% of the total HM load associated with RDS on the two selected roads were easily washed away after rainfall. The average load of each heavy metal at S1 point was in the order of Cd>Mn>Zn>Co>Cu>Pb>Ni>Cr and Fe. Among them, Cd (43.7%), Mn (41.4%) and Zn (40.3%) had the greatest impact on the pavement. The average washout of HMs ranged from 16.6 to 46.3% relative to the total mass of HMs associated with RDS (Pb, Zn, Cu, Cr, Ni, Cd, Fe, Mn, Co and Ba) in the dry season. Cd, Mn and Zn had the highest washout effect in the study area. The particle size gradation below 250 μm contributed 86.2% of the heavy metal load. 7% of the potential heavy metal load was washed out from RDS. The results using the simplified method showed that the potential load of RDS HMs washed away by runoff averaged between 46.3% and 16.6%, with the percentage of total mass relative to HMs in the dry season RDS, among which Cd, Mn, and Zn were the most susceptible HMs to washout.

[0168] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A simplified method for estimating heavy metal pollution loads in urban road storm runoff; characterized in that: The steps include: Step 1: Select the corresponding sampling process according to the dry season and the rainy season. In the dry season, execute step A2, and in the rainy season, execute step B2; Step A2, sampling dry season road sediment (RDS); Step A3, analyzing the particle size composition characteristics of the sample; Step A4, performing a leaching test on dry season road sediment (RDS); Step A5, obtaining a representation of the dry season road sediment (RDS) load; Step A6, evaluating the heavy metal load of the road sediment under dry weather; Step A7: Simplify the flushing equation and then proceed to step 8; The constructed simplified washoff equation was used to calculate the heavy metal pollution load of storm runoff originating from dry season road sediment (RDS), and the equation expression is: Wherein, TLW is the potential load of heavy metals (HM) washed down by runoff from dry season road sediment (RDS), expressed as a percentage; LW <250,i is the percentage of dry season road sediment (RDS) susceptible to runoff transport at 250 μm; ML <250,i The percentage of heavy metals associated with dry season road sediment (RDS) at 250 μm; LE<250 refers to the leaching rate of associated heavy metals in dry season road sediment (RDS) at 250 μm; LE ≥250 It represents the percentage of heavy metal leaching when the dry season road sediment (RDS) is greater than or equal to 250 μm; Polynomial (1-LW <250,i / 100) represents the proportion of dry season road sediment (RDS) below 250 μm that is not lost by runoff; Step B2, sampling the road runoff in the rainy season; Step B3, using the Manning equation to calculate the instantaneous flow; Step B4, event mean concentration (EMC) analysis; Step B5, analysis of heavy metals in dissolved and particulate phases of road runoff during the rainy season; Step B6, instantaneous concentration linear interpolation calculation; Step 8: Construct the estimation method.

2. The simplified method for estimating heavy metal pollution load of urban road storm runoff according to claim 1 is characterized in that: In step A2, the sampling area for sampling dry season road sediment (RDS) is 0.50m 2 , the size of the sampling surface area is ensured by placing an acrylic frame on the surface; Using a vacuum cleaner of 1.5 kW or more in series with a plastic fiber brush, retaining particles larger than 1 μm as the sample; Sampling of dry season road sediment (RDS) consists of two stages; The first stage involves tertiary sediment extraction directly from the road surface; The second stage is to sweep the same surface with a plastic fiber brush; The sampling surface is swept and the same force is applied throughout the entire sampling process; The total road sediment load was calculated as the sum of dry season road sediment (RDS) collected in two phases.

3. The simplified method for estimating heavy metal pollution load of urban road storm runoff according to claim 1 is characterized in that: In step A3, the particle size distribution of dry season road sediment (RDS) is determined using the ISO-11277 method; The mesh size for particle size distribution (PSD) analysis in road sediment is between 63 μm and 2800 μm, specifically including particles that can pass through a 63 μm mesh, a 63 μm to 125 μm mesh, a 125 μm to 250 μm mesh, a 250 μm to 500 μm mesh, a 500 μm to 1000 μm mesh, a 1000 μm to 2000 μm mesh, and a 2000 μm to 2800 μm mesh; Statistical analysis was performed using the Kolmogorov-Smirnov test to assess the normality of the particle size distribution (PSD) dataset in road sediments and logarithmic transformation was performed to assess the adjustment of the probability distribution; Linear regression analysis was used to establish a model between particle size and the mass percentage of dry season road sediment (RDS) in each particle size class.

4. The simplified method for estimating heavy metal pollution load of urban road storm runoff according to claim 1 is characterized in that: In step A4, deionized water was mixed with dry season road sediment (RDS) at an L / S ratio of 10 L / kg dry matter; Mechanical shaking at 10 rpm for 24 h in the dark at room temperature; After completion, the supernatant was filtered using a polytetrafluoroethylene (PTFE) membrane filter to obtain the dry season road sediment (RDS) leachate; The potential conditions of turbulent runoff on dry season road sediment (RDS) were simulated in the leaching test, and the determination of heavy metals in dry season road sediment (RDS) and leachate was carried out according to HJ / T 350-2007 and HJ 700-2014 methods.

5. The simplified method for estimating heavy metal pollution load of urban road storm runoff according to claim 1 is characterized in that: Before step B4, samples are collected within 24 to 48 hours after the road runoff event occurs; after returning to the laboratory, they are pre-processed as follows: Stirring the sample; The remaining sample in the collection container was then acidified with 0.5% HNO3 and stored at room temperature for at least one week before in-house analysis.

6. The simplified method for estimating heavy metal pollution load of urban road storm runoff according to claim 5 is characterized in that: After shaking the pretreated sample, two 10 mL dissolved phase samples were taken directly from the bottle; and filtering the solutes in the subsample to a maximum particle size not exceeding 0.45 μm; The remaining samples except for the two described subsamples were pre-filtered through a 250 μm nylon mesh, standardized according to maximum particle size, and then passed through a pre-weighed and dried 0.45 μm membrane filter using a vacuum filtration manifold; The membrane filter is unsieved nitrocellulose; After completion, the filtered filter paper was dried in an oven at 60°C; After completion, it was digested in batches using aqua regia and used as the particle phase; Finally, the digested sample was made up to 15 mL with ultrapure water, and 10 mL of 0.45 μm filtered subsample was taken out for analysis; all subsamples were refrigerated before analysis.

7. The simplified method for estimating heavy metal pollution load of urban road storm runoff according to claim 6 is characterized in that: The sample metals were analyzed using Varian axial inductively coupled plasma atomic emission spectrometry (ICP-AES); The total concentration was calculated as the sum of the concentrations in the dissolved phase and the particulate phase, i.e., after digestion.

8. The simplified method for estimating heavy metal pollution load of urban road storm runoff according to claim 7 is characterized in that: Time series of flow and pollutant concentrations were upsampled from variable interval data and sampling times to 1-min intervals; The total load for each event was calculated by linear interpolation of the instantaneous concentrations determined with a 1-min resolution and by logarithmic calculations of the given pollutant generation rate curves, which were normalized for rainfall depth and road area.

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