Influence of Major Urban Construction on Atmospheric Particulates and Emission Reduction Measures
2)Key Laboratory of Transportation Tunnel Engineering of the Ministry of Education, Southwest Jiaotong University, Chengdu 610031
3)West China Hospital of Stomatology, Sichuan University, Chengdu 610041
4)School of the Earth Sciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 610031
Copyright ⓒ 2018 by Asian Journal of Atmospheric Environment
This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
In order to understand the variation of air quality and the concentration of atmospheric particulates in Chengdu Second Ring Road renovation project, this paper starts to investigate the surrounding residents’ opinions on the influenced environment and their daily lives via questionnaires. Then the study numerically simulates the change rule of atmospheric particulates in terms of time and space by using the Gaussian dispersion-deposition model and the compartment model. The optimized scientific scheme is selected by the improved fuzzy analytical hierarchy process (FAHP) to help decision making for the future urban reconstructions. Finally, the reduced emissions of atmospheric particulates are measured when the improvement scheme is provided. According to the study, it can be concluded that the concentration of atmospheric particulates increases rapidly in central Chengdu city during the renovation project, which results in worsening air quality in Chengdu during March 2012 to March 2013. Taking related measures on energy saving and emission reduction can effectively reduce the concentration of atmospheric particulates and promote economic, environmental and social coordination.
Keywords:
Atmospheric particulates, Numerical simulation, Gaussian dispersion deposition model, Compartment model, Improved FAHP1. INTRODUCTION
At present, airborne particulate pollution, mainly resulting from fugitive dust in city, remains rather serious based on relevant studies. When absorbed into human body for a long time, the particulate may affect the public’s health. Chengdu lies in the middle part of Sichuan Province with humid climate and it is endowed with a lot more rainfall in summer. Special topographic structure makes days of calm wind constitute a percentage from 32% to 55% against the whole year. The Chengdu Second Ring Road, whose surrounding regions are the central areas of Chengdu city, lies in the heart of Chengdu Plain with large flow of people and quantities of habitants. Apart from that, Chengdu has weak wind, inactive air, stable atmosphere, and weak turbulent motions.
The renovation project of Chengdu Second Ring Road began in March, 2012, becoming the most influential city road project with the widest range of construcftion. But the project was bringing along the increasing pollution sources, for example the natural source, source of industrial dust, source of transportation and source of daily lives etc. According to the previous studies, the change level of the airborne particulates derived from the related sources presents a linear variation with the related sources within a certain time. Therefore, the study only takes the concentration of airborne particulates caused by the renovation project into account (Kong, 2010).
2. RESEARCH APPROACH
This paper makes statistical analysis on the questionnaire survey filled out by the residents around the Second Ring Road based on their personal perception to the changes of the air quality of Chengdu, while these changes resulted from airborne particulates sourced from the renovation project. In addition, the level of airborne particulates is measured before and after the renovation. Besides, the diffusion model of airborne particulates is established. At last, the optimal solution for future decision making on constructions is put forward on the basis of comprehensive investigation and the detailed analysis of ambient air quality.
This research centers on the theme of energy saving and emission reduction by studying air pollution from road renovation, and then provides suggestions on energy saving and emission reduction based on computer simulation. The technology roadmap is shown in Fig. 1 as followed.
3. RESEARCH RESULTS AND DISCUSSIONS
3. 1 Results of Questionnaire Survey
A questionnaire survey is used to investigate the publics’ perception to the renovation of Chengdu Second Ring Road. The followings were investigated:
- • Attention of the investigated on recent environmental quality in Chengdu;
- • Satisfaction of the investigated on recent environmental quality in Chengdu;
- • Satisfaction of the investigated on the pollution control of the project;
- • Opinion and evaluation of the investigated on the measures taken in different stages of the project;
- • Prediction of the investigated on air quality after taking measures and their evaluations on research meaning.
The questionnaire was designed according to the experiences at home and abroad, combined with the present situations in Chengdu. Fig. 2 shows the basic steps of the questionnaire survey. The questionnaire was designed according to the relative empirical literatures home and abroad, combined with the present situations in Chengdu.
Firstly, we did pre-survey in several places, such as Jiuli Campus of Southwest Jiaotong University, Guanghua Campus of Southwestern University of Finance and Economics, Renmin South Road Interchange, Chengdu North Railway Station, and etc., aiming to have a basic understanding on the impacts of the renovation project on surrounding environment and nearby residents. Then, according to the problems in the process of investigation, we modified our questionnaire, especially adjusting the measures of emission reduction. After that, the questionnaire with 18 questions was distributed in three different ways. The first was an on-line questionnaire survey. The second was to distribute the questionnaire to friends, professors and experts from environmental science. The last was an on-site questionnaire survey.
According to the statistical results of the survey, over a half of respondents thought the environmental quality was bad or terrible in Chengdu city and that air pollution greatly affected their daily lives during the renovation project. Besides, nearly two thirds of the respondents thought the current control and prevention measures for the renovation project were far from satisfactory.
As shown in Fig. 3, the respondents pay much attention to the quality of atmospheric environment of the city where they live. Only 4.23% of the respondents have never noticed it.
Likert scale is a useful method to gather the basic information of public opinions in doing researches or making surveys (Qi, 2006). It is balanced on both sides of a neutral option, creating a less biased measurement. In our investigation about satisfaction, the respondents’ opinions can be classified into five levels: excellent, good, general, bad and terrible.
When asked about their satisfaction on the environmental quality, more than 50% of the respondents held the view that the environment quality is bad or terrible in recent months, while only a few respondents (9.22%) considered the environment good or excellent. Therefore, citizens generally believed that environmental quality in this city fails to reach a good standard, and taking measures on the renovation is of urgent necessity.
Fig. 5 shows that about one-third of the respondents thought that atmospheric pollution was the biggest environmental problem to their normal lives, followed by noise pollution (23.31%) and water pollution (21.15%).
By comparing people’s satisfaction of Chengdu environmental quality (Fig. 4) and control works of the project (Fig. 6), we can clearly find out that a large percentage of people considered these two indexes unsatisfactory.
As shown in Fig. 7, 38.94% of the investigated believed that the most serious environmental pollution caused by the renovation project is air pollution. It can be seen that the renovation project and environmental quality in Chengdu, especially atmospheric quality, are closely connected. Residents almost withstand atmospheric pollution problems everyday, therefore, it is significant to take appropriate measures to reduce pollution and improve the air quality during the construction period.
Considering the relationship between economy and benefit, nearly all of the respondents believed that the most effective measure to reduce atmospheric particulates during the renovation period is to strictly control the exposure of fine dust, such as cements (Fig. 8). And over 50% of the respondents thought that increase of artificial air humidifying may also be a great way to reduce the concentration of the atmospheric particulate in a short time and deserve strongly promoting and popularizing.
When it comes to the point of decreasing dust exposure, statistics in Fig. 9 show the measures in detail. Considering both economy and benefit, more than 1,200 of the investigated preferred the way of regular manual watering, regular ground cleaning and sealing up or coverage of exposed loads easy to produce dust of motor vehicles. In addition, about 1,000 people also considered the coverage of exposed windrow easy to produce dust a good way and to be worth promotion during construction.
As shown in Fig. 10, approximately three quarters of the respondents suggested that the government should continue to carry out the measures of traffic restrictions, targeting to cut down the vehicle emissions near the construction site. Meanwhile, more than a half of them advised that planting more plants and growing more grass to absorb tiny particles and dusts in construction area can improve the environmental self-purification capacity to a great extent.
Furthermore, 71.78% of the investigated believed that air quality will gradually improve in one year after taking the measures mentioned above, while only 0.55% of the respondents thought it would become worse, as shown in Fig. 11 and Fig. 12. It can be seen that the measures are useful and meaningful during the renovation period in citizens’ views. And over 90% of the respondents thought that the questionnaire survey we did had great significance to regional environment and the percentage of the respondents uncertain about the effects is merely 1.96%.
3. 2 Application of Gaussian Dispersiondeposition Model
In order to simulate transmission of air pollution caused by the renovation project in the surrounding areas, we imported the Second Ring Road map of Chengdu into Matlab, utilizing Canny Edge-detection to produce the abstract map of the Second Ring Road of Chengdu (Wang, 2009), as shown in Fig. 13.
Gaussian dispersion model was presented on conditions that the concentration of pollutants was submitted to normal distribution. After quantities of continuous point sources diffused into a small scale, especially from the test in the plain and some flat regions, it can be indicated their concentration distribution is near normal distribution.
We discretized the map of the Second Ring Road and got the discretized continuous point source model of the Second Ring Road by conducting edge-detection. The average wind speed of Chengdu is only 1.4 m/s, and by adopting the Gaussian dispersion-deposition model, we obtained the pollution diffusion model of the Second Ring Road and contour line of pollution diffusion.
The pollution diffusion model is derived by overlapping pollution throughout the Second Ring Road. It can be seen that the surrounding areas of the Second Ring Road are affected. Certainly, other areas are also influenced to some extent.
3. 3 Application of Compartment Model
Usually compartment is used in pharmacokinetics to simulate human body under the principle that any part of human body can be classified into the same compartment as long as its rate of absorbing or eliminating drug remains the same. Similarly, this paper referred from this principle, regarding pollution from the renovation as drug while regarding the atmosphere, soil, water and surrounding conditions as the compartment. Later, we established the compartment model of pollution diffusion of Chengdu by mechanism analysis and at the same time established corresponding differential equations and, by solving them, functional relation of Chengdu airborne pollutants varying with time passing was obtained. We compared the functional relation with the statistics from the practical study, which shows to be consistent.
The diffusion model of the Second Ring Road and the surrounding conditions are shown in Fig. 16. The environment purification capacity was categorized into chamber III, regarding it as purification with no feedback.
The differential equations shown in the following is derived from compartment model.
$$$\left\{\begin{array}{c}\frac{\text{d}{x}_{1}\left(t\right)}{\text{d}t}={-k}_{12}{x}_{1}\left(t\right)-{k}_{13}{x}_{1}\left(t\right)+{k}_{21}{x}_{2}\left(t\right)+{f}_{0}\left(t\right);\hfill \\ \frac{\text{d}{x}_{2}\left(t\right)}{\text{d}t}={k}_{12}{x}_{1}\left(t\right)-{k}_{21}{x}_{1}\left(t\right);\hfill \\ {x}_{1}\left(0\right)={Z}_{1};\hfill \\ {x}_{2}\left(0\right)={Z}_{2};\hfill \end{array}\right.$$$ | (1) |
Eq. (1) is solved by using Matlab.
As shown in Fig. 17, solid line stands for the trend of air pollution index in the surrounding areas of the Second Ring Road ranging from March, 2011 to March, 2013 while the dashed line stands for the trend of air pollution index predicted from the model. The atmospheric particulate is the primary pollutant in Chengdu. Therefore Air Pollution Index (API) and Particulate Pollution Index are regarded as the same.
Air pollution index would have been decreased to one fourth of the previous one to make air quality of Chengdu reach a good standard if f_{o}(t) were decreased and at the same t me k_{13} were increased.
3. 4 Application of the Improved Fuzzy Analytic Hierarchy Process (FAHP)
According to Gaussian dispersion simulation, it is suggested that the pollution situation surrounding the Second Ring Road is serious, and the results of the compartment model indicate that it will take a long time for the air quality to restore to the original state. Due to a lack of experience in pollution treatment, there are many problems in control measures regarding air pollution.
In addition, people may have uncertainties to clearly understand the air pollution. Therefore, we establish an improved FAHP to propose and screen treatment schemes for atmospheric particulates. Zhao et al. (2011) according to the characteristics of urban traffic environment, a comprehensive evaluation index system for urban traffic environment is established. Then use the fuzzy analytic hierarchy process to determine the weights of the specific indicators in the evaluation model, and make a comprehensive evaluation of the development trend of the urban traffic environment. Taking Shanghai as an example for verification, the correctness of the model, the rationality of the evaluation index system and the feasibility of the evaluation method are analyzed. Chen et al. (2012) used the improved AHP method to determine the weighted average method of weights to evaluate the status of heavy metal pollution in typical vegetable fields in the suburbs of Nanjing. AHP (Analytic Hierarchy Proces s) and FAHP (Fuzzy Analytic Hierarchy Process) are used to choos between Barcode and RFID system for the company warehouse data collection system (Erkan et al., 2014). The flowing processes are shown in Fig 20.
3. 4. 1. 1 Proposal of Feasible Schemes
Four feasible schemes are proposed in this paper after we take Chengdu topographic, climatic features, geological structure, expert opinion, investigation conclusion and the aim of energy saving and emission reduction into account, as hown in Table 2
This paper adopts the improved FAHP to select the appropriate treatment schemes for atmospheric particulates (Kang et al 2010) The establishment of Comprehensive Evaluation Index System of the improved FAHP is shown in Fig. 21.
In the improved FAHP, through comparison between every two indexes, the fuzzy judgment matrix A=(a_{ij})_{n×n} is obtained:
- 1) a_{ii} = 0.5, i=1, 2, ⋯ n;
- 2) a_{ij} + a_{ji} = 1, i=1, 2, ⋯ n
The matrix usually adopts 0.1-0.9 to distribute the index weights, as shown in Table 3.
3. 4. 1. 2 Establishment of Mathematical Model
The fuzzy complem entary judgment matrix is show as follows through comparison between every two indexes a_{1}, a_{2}, ⋯ a_{n}·
$$$\mathit{A}=\left[\begin{array}{ccc}{a}_{11}\hfill & \cdots \hfill & {a}_{1n}\hfill \\ \vdots \hfill & \ddots \hfill & \vdots \hfill \\ {a}_{n1}\hfill & \cdots \hfill & {a}_{nn}\hfill \end{array}\right]$$$ | (2) |
STEP 1 By calculating each sum of every row of statistics of judgment matrix using the formula $$ {r}_{i}=\sum _{k=1}^{n}{a}_{ik},\left(i=\mathrm{1,2},\cdots ,n\right)$$, and then conducting the following mathematical substitution.
$$${r}_{ij}=\frac{{r}_{i}-{r}_{j}}{2\left(n-1\right)}+0.5$$$ | (3) |
We can obtain the consistent matrix R=(r_{ij})_{n×n}·
STEP 2 Normalizing rank
$$$\mathit{W}={\left({w}_{1},{w}_{2}\dots ,{w}_{n}\right)}^{\mathrm{T}}$$$ | (4) |
If
$$${W}_{i}=\frac{\sum _{j=1}^{n}{a}_{ij}}{\sum _{i=1}^{n}\sum _{j=1}^{n}{a}_{ij}}\left(i,j=\mathrm{1,2},\cdots ,n\right)$$$ | (5) |
Eq.(5) is the formula calculating the judgment matrix weight vector.
Let G_{n} be the set of all judgment matrix. And we supposed A=(a_{ij})_{n×n} and B=(b_{ij})_{n×n} ∈ G_{n}, used norm $$ \mathit{A}-\mathit{B}=\sum _{i=1}^{n}\sum _{j=1}^{n}\left|{a}_{ij}-{b}_{ij}\right|$$ to represent distance between A and B and denoted it by ρ(A, B)=A-B.
STEP 3 The consistency check of the judgment matrix (Xu, 2002).
To estimate the reasonability of the weight, we need a further check of the consistency of the judgment matrix.
Definition: Matrix A=(a_{ij})_{n×n} and B=(b_{ij})_{n×n} are both the judgment matrix.
$$$I\left(\mathit{A},\mathit{B}\right)=\frac{1}{{n}^{2}}\sum _{i=1}^{n}\sum _{j=1}^{n}\left|{a}_{ij}-{b}_{ij}\right|$$$ | (6) |
Denoting I(A, B) as the compatibility index of sum.
W=(w_{1}, w_{2}, ⋯, w_{n})^{T} is weight vector of the judgment matrix, of which $$ \sum _{i=1}^{n}{W}_{i}=1,{w}_{i}\ge 0\left(i=\mathrm{1,2},\cdots ,n\right)$$.
$$ Let{w}_{ij}=\frac{{w}_{i}}{{w}_{i}+{w}_{j}}\left(\forall i,j=\mathrm{1,2},\cdots ,n\right)$$ be N-matrixn.
$$${\mathit{W}}^{*}={\left({W}_{ij}\right)}_{n\times n}$$$ | (7) |
W* denotes characteristic matrix for the judgment matrix A.
α denotes the attitude of the decision maker. Judgment matrix is considered to be satisfyingly consistent when compatibility index I(A, W*) ≤ α. The less α is, the higher decision maker asks of the consistency of fuzzy matrix. In this paper, α = 0.1
Multiple comparing judgment matrix based on the same element set X is generally given by multiple experts on practical issue.
$$${\mathit{A}}_{\mathit{k}}={\left({a}_{ij}^{\left(k\right)}\right)}_{m\times n},k=\mathrm{1,2},\cdots ,m$$$ | (8) |
We can obtain every weight set W_{k}.
$$${\mathit{W}}_{\mathit{k}}=\left({W}_{1}^{\left(k\right)},{W}_{2}^{\left(k\right)},\cdots ,{W}_{n}^{\left(k\right)}\right),k=\mathrm{1,2},\cdots ,m$$$ | (9) |
Then in order to check the consistency of the judgment matrix:
- 1) Check the satisfying consistency of A_{k}
$$$I\left({\mathit{A}}_{\mathit{k}},{\mathit{W}}^{\left(k\right)}\right)\le a.k=\mathrm{1,2},\cdots ,m$$$ | (10) |
- 2) Check the satisfying consistency of matrixes
$$$I\left({\mathit{A}}_{\mathit{k}},{\mathit{A}}_{\mathit{l}}\right)\le a,k\ne l,k,l=\mathrm{1,2},\cdots ,m$$$ | (11) |
It can be proved that the judgment matrix A_{k}(k=1, 2, ⋯, m) is consistent and acceptable. That means if Eqs. (10) and (11) are satisfied, considering the mean value of m audiencce sets as the distributed weight vector for element set X is reasonable and reliable. Formual for weight vector
$$$\mathit{W}=\left({\mathit{W}}_{1},{\mathit{W}}_{2},\cdots {\mathit{W}}_{n}\right)$$$ | (12) |
meets
$$${W}_{i}=\frac{1}{n}\sum _{k=1}^{m}{w}_{i}^{\left(k\right)},i=\mathrm{1,2},\cdots ,n$$$ | (13) |
3. 4. 2. 1 Confirmation of Weight Matrix
As for the four evaluation criteria mentioned, suppose there are two experts of this field made comparison between every two indexes on every factor according to scoring method in Table 3, we can obtain weight judgment matrix A_{1} as follows:
According to the Eq. (3), its weight vector is as follows:
$$ {W}_{1}=\left(0.24440.27220.21670.2667\right)$$
According to the Eq. (5), the calculation of characteristic matrix W_{1}^{*} is as follows:
$$ {w}_{1}^{*}=\left[\begin{array}{c}0.5000\hfill \\ 0.5269\hfill \\ 0.4699\hfill \\ 0.5217\hfill \end{array}\begin{array}{c}0.4731\hfill \\ 0.5000\hfill \\ 0.4432\hfill \\ 0.4948\hfill \end{array}\begin{array}{c}0.5301\hfill \\ 0.5568\hfill \\ 0.5000\hfill \\ 0.5517\hfill \end{array}\begin{array}{c}0.4783\hfill \\ 0.5052\hfill \\ 0.4483\hfill \\ 0.5000\hfill \end{array}\right]$$
According to the Eqs. (10) and (11), the compatibility index of A_{1} and W_{1}^{*} is: I(A_{1}, W_{1}^{*}) = 0.0884 < 1, thus the weight W_{1} is distributed reasonably.
A_{2} denotes the judgment matrix given by expert 2, as is shown in Table 5.
In a similar way, the weight vector can be calculated as:
$$ {W}_{2}=\left(0.26110.27220.22780.2389\right)$$
The calculation of the characteristic matrix W_{2}^{*} is as follows:
$$ {A}_{2}=\left[\begin{array}{c}0.5000\hfill \\ 0.5104\hfill \\ 0.4659\hfill \\ 0.4778\hfill \end{array}\begin{array}{c}0.4896\hfill \\ 0.5000\hfill \\ 0.4556\hfill \\ 0.4674\hfill \end{array}\begin{array}{c}0.5341\hfill \\ 0.5444\hfill \\ 0.5000\hfill \\ 0.5119\hfill \end{array}\begin{array}{c}0.5222\hfill \\ 0.5326\hfill \\ 0.4881\hfill \\ 0.5000\hfill \end{array}\right]$$
Compatibility index of A_{2} AND W_{2}^{*} is: I(A_{2}, W_{2}^{*}) = 0.0805 < 1, thus the weight W_{2} is distributed reasonanly.
Meanwhile, the satisfying compatibility of matrix A_{1} and A_{2} is checked according to the Eqs. (10) and (11). That is: I(A_{1}, A_{2}) = 0.0875 < 1, thus the judgment matrices are identified to be consistent.
Synthesizing opinions of the two experts, weight vector W can be computed according to the Eq. (13):
$$ \begin{array}{c}W=\frac{1}{2}\left[\left(0.2444+0.2611\right)\left(0.2722+0.2722\right)\left(0.2167+0.2278\right)\left(0.2667+0.2389\right)\right]\hfill \\ =\left(0.25280.27220.22330.2528\right)\hfill \end{array}$$
3. 4. 2. 2 Establishment of Judgement Matrix
Establishing judgment matrix on the four schemes and adopting the scoring method given by experts in Table 3, we can obtain fuzzy matrix by comparison between every two indexes. To make it clear, this paper only shows one judgment matrix scored by one expert.
With the four judgment matrices shown above, the sort vector can be obtained for the four schemes according to Eq. (7).
$$ \begin{array}{c}{X}_{1}=\left(0.25000.23330.22780.2889\right)\hfill \\ {X}_{2}=\left(0.26110.23330.21110.2944\right)\hfill \\ {X}_{3}=\left(0.23330.25000.23330.2833\right)\hfill \\ {X}_{4}=\left(0.26670.26670.25560.2111\right)\hfill \end{array}$$
The judgment matrix can be obtained according to the four sort vectors, as shown in Table 10.
3. 4. 2. 3 Result of Sorting on Schemes
$$ \begin{array}{c}B=W\times R\hfill \\ ={\left[\begin{array}{c}0.2528\hfill \\ 0.2722\hfill \\ 0.2333\hfill \\ 0.2528\hfill \end{array}\right]}^{T}\times \left[\begin{array}{c}0.2500\hfill \\ 0.2611\hfill \\ 0.2333\hfill \\ 0.2667\hfill \end{array}\begin{array}{c}0.2333\hfill \\ 0.2333\hfill \\ 0.2500\hfill \\ 0.2667\hfill \end{array}\begin{array}{c}0.2278\hfill \\ 0.2111\hfill \\ 0.2333\hfill \\ 0.2556\hfill \end{array}\begin{array}{c}0.2889\hfill \\ 0.2944\hfill \\ 0.2833\hfill \\ 0.2111\hfill \end{array}\right]\hfill \\ =\left(0.25360.24550.23150.2695\right)\hfill \end{array}$$
According to maximum membership degree, the optimized rank of the alternative four schemes is: scheme 4>scheme 2>scheme 3. And the final scheme involves eight methods: regular manual watering, regular ground cleaning, further promotion of traffic restrictions, prohibition of construction within a certain wind speed, employment of green building materials, slower speed of vehicles, better tree planting and grass growing around construction site, and intensity of local supervision.
4. ANALYSIS OF ACTUAL RESULTS
4. 1 Evaluation and Analysis Method
Emission loads of airborne particulates to be reduced after using the treatment scheme are calculated directly or indirectly on the basis of previous literatures. Later, the actual economic benefit of this scheme to the major urban construction project is evaluated.
4. 2 Analysis of Concrete Effects
Calculation of the dust resulted from a single vehicle:
$$${e}_{1}=0.000501\times V\times 0.83\times U\times \left(T\u22154\right)$$$ | (14) |
In the Eq. (14), e_{1}(kg/km) stands for the emission factor of road dust resulting from a single vehicle; V(km/h) stands for the average speed of vehicles passing by, and in general adopts 40km/h; U(m/s) stands for wind speed causing the dust, in general adopting 5m/s; T stands for average tires of a vehicle, in general adopting 4.
The road dust loads involving large quantities of particulates resulted from a single vehicle, which can be calculated by the Eq. (14):
$$ \begin{array}{c}{e}_{1}=0.000501\times 40\times 0.823\times 5\times 0.139\times \left(4\u22154\right)\hfill \\ =0.012\text{kg/km}\hfill \end{array}$$
Qin(2010) analyzed the effect of spray-sprinkling on inhibiting road dust based on Linfin City, Shanxi Province, Linfen, a key environmentally-protected city, national environmental monitoring network and energy and chemical industry base, has suffered from serious air pollution. Ambient air pollution is characterized mainly by SO_{2}, and inhalable particles. Particulate pollution is a key factor for urban air pollution. From 2007, street sprinklers can inhibit 85% dust loads by regularly carrying out spraying and sprinkling. Figs. 22 and 23 show the change of air quality and airborne inhalab be drawn that spray-sprinkling is an effective measure on inhibiting road dust.
The total length of Chengdu Second Ring Road is 28.3km. The average number of vehicles hitting the Second Ring Road is 1,000. Therefore, it can be calculated that the total dust loads are E_{1}=244.51t/month(suppose 30 days to be a month).
The rate of restraining dust was adopted as w_{1} = 85%. Therefore, the monthly dust loads restrained were calculated in the following:
$$ {W}_{1}={w}_{1}{E}_{1}=207.83\mathrm{t}\u2215\mathrm{m}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{h}$$
Adopting traffic restrictions can effectively reduce the traffic flow. And the road dust loads can be greatly reduced by inhibiting sediment vehicles from passing by. These two measures can significantly decrease the road dust loads.
Tian et al. (2009) studied the influence of traffic restrictions on dust emission round the road and construction sites during the four days of traffic restrictions in Beijing, in August, 2007. They quantified the improvement level of urban air quality by using the traffic restriction compared with the statistics seven days ago. Airborne dust concentration around the road has approximately decreased by 70% during the restriction period in Beijing. In addition, compared with the statistics 20 days ago, the airborne dust concentration has been cut down by about 38%.
Referring to Bulletin on the State of the Environment in Chengdu published by Chengdu Environment Protection Bureau(CDEPB) in 2012, the average dustfall content in Chengdu city is 9.23t/km^{2}. An area of 113.2km^{2} in the surrounding region is covered in our research, which is within 2,000 meters of the Second Ring Road. Therefore, the dustfall content in surrounding region was E_{2} = 1044.84t/month. If adopting the traffic restriction measure, it can be supposed that the reduction rate of airborne dust concentration within a month around the Second Ring Road was w_{1} = 40%. As a result, the dustfall content after taking this measure was:
$$ {W}_{2}={w}_{2}{E}_{2}=417.94\mathrm{t}\u2215\mathrm{m}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{h}$$
Wind speed should be taken into account when considering the diffusion of atmospheric dust. Tian et al. (2008) studied on the relationship between diffusion of fugitive dust source and wind speed on a major construction site. This study considered construction and road dust as typical man-made dust sources with a result shown in Fig. 24. Through numerical fitting, the functional relationship between PM_{10} concentration c(μg/m^{3}) and wind speed ν (m/s) is shown as following:
$$$c\left(v\right)={-17.54v}^{3}+{192.3v}^{2}-435.1v+33939$$$ | (15) |
According to the functional relationship, it can be concluded that PM_{10} concentration has been decreasing gradually with growing wind speed. PM_{10} concentration reached to a valley with wind speed ranging from 1.2m/s to 1.6m/s. With regression equation, the exact wind speed was calculated to be 1.4m/s when PM_{10} concentration reached to the valley. At this time, the corresponding wind scale is about two. Correspondingly, the harm of man-made dust on air quality reached to the lowest. It can be further drawn that wind speed plays a big role in the atmospheric dust diffusion and the countermeasures on controlling the man-made dust.
According to the statistics of Chengdu meteorological data, the weather in Chengdu is mostly calm with average yearly wind speed being 1.4 m/s, and the maximum wind speed being 12 m/s. Corresponding PM_{10} concentration of the average wind speed is as follows:
$$ c\left({\nu}_{0}=1.4\right)=-17.54\times {1.4}^{3}+192.3\times {1.4}^{2}-435.1\times 1.4+339.9=59.54$$
In general, the PM_{10} concentration can remain a relative low level as long as construction is implemented without strong wind. But for other cities, for example, Lanzhou, Beijing and so on, the average yearly wing speed is usually over 3m/s. We took ν_{1}=3m/s into the Eq. (15).
$$ c\left({\nu}_{1}=3\right)=-17.54\times {3}^{3}+192.3\times {3}^{2}-435.1\times 3+339.9=291.72$$
The calculated value is about five times larger than the one c_{min} appearing at the valley. Therefore, the construction should be inhibited under a certain wind speed. But for those cities like Chengdu with high rate of calm wind, the effect would be little after adopting this measure. As a result, the total atmospheric dust loads can be regarded as unchanged(W_{3} = 0).
In order to quantitatively describe the influence of vehicle speed on the fugitive dust around the unpaved roads, Fan et al. (2012) calculated the dust emission load based on field test data. Besides, they discussed the relationship between vehicle speed and dust emission around the unpaved roads.
PM_{10} emission load emitted by a single car per kilometer is defined as PM_{10} emission factor; detailed calculating method can be found in Fan’s study. According to the test of the relationship between the emission factors of different types of vehicles and vehicle speed, the result is shown in Fig. 25.
According to the figure, it can be concluded that emission factor increases with vehicle speed growing and that the emission factor of oversize vehicles is apparently higher than smaller ones. The linear relationship exists between the emission factor of these two kinds of vehicles and vehicle speed. The functional relationship between PM_{10} emission factor y(g) and vehicle speed x(km/h) can be derived as follows:
$$$\left\{\begin{array}{cc}{y}_{1}=10.80x\hfill & {R}^{2}=0.325;\hfill \\ {y}_{2}=4.968x\hfill & {R}^{2}=0.706;\hfill \end{array}\right.$$$ | (16) |
Therefore, road dust can be effectively reduced as long as transport vehicles are reasonably selected and vehicle speed is properly controlled. Under the condition that oversize vehicles and smaller ones can both meet the transport demands, there is an urgent need to realize miniaturization of vehicles.
Suppose that through reallocation of vehicles traffic volumes, say, 20 oversized vehicles being replaced by same number of smaller ones, and vehicle speed slowing down to 50km/h from 30km/h, it can be seen that the reduction volume ∆y of the PM_{10} emission factor is as follows:
$$ \u2206y=10.80\times 50-4.968\times 30=391$$
Suppose that a truck travels a distance of 200km everyday on average, then, total monthly PM_{10} emission loads W_{4} to be reduced after taking this measure can be calculated as follows:
$$ {W}_{4}=391\times 20\times 200\times 30\mathrm{g}/\mathrm{m}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{h}=46.92\mathrm{t}/\mathrm{m}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{h}$$
4. 3 Conclusion of Comprehensive Effects
Based on the ideal hypotheses in 4.1, the emission volume W_{sum} of airborne particulates to be reduced after adopting all the prevention measures on emission reduction can be calculated by the following formula:
$$${W}_{\mathrm{s}\mathrm{u}\mathrm{m}}=\sum _{i=1}^{k}{W}_{i}$$$ | (17) |
In the formula, k stands for number of actual measures in the treatment scheme; W_{i} stands for the emission volume of airborne particulates to be reduced after adopting every measure, and i = 1, 2, …, k.
As for the renovation project, the total emission volume of airborne particulate to be reduced after adopting the optimal scheme can be obtained:
$$ {W}_{\mathrm{s}\mathrm{u}\mathrm{m},0}={W}_{1}+{W}_{2}+{W}_{3}+{W}_{4}=207.83+417.94+0+46.92=672.69\mathrm{t}/\mathrm{m}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{h}$$
Suppose this value is the average monthly value during construction, the construction period T of renovation project in the Second Ring Road is 14-month (from March, 2012 to May, 2013). As a result, the optimal scheme can reduce the total emission loads of airborne particulates(AP) surrounding the Second Ring Road as followed:
$$ Q={TW}_{\mathrm{s}\mathrm{u}\mathrm{m},0}=14\times 672.69=9417.66\mathrm{t}$$
It can be seen that the optimal scheme can effectively reduce the urban airborne particulates.
5. CONCLUSION
According to the statistical results of the survey, over a half of respondents thought the environmental quality was bad or terrible in Chengdu city and that air pollution greatly affected their daily lives during the renovation project. Besides, nearly two thirds of the respondents thought the current control and prevention measures for the renovation project were far from satisfactory. According to the study of Gaussian dispersion-deposition model, it can be found that, with the renovation project put to operation, airborne particulates in Chengdu has increased significantly and still appears to be growing increasingly in the near future. By the end of March in 2013, particulate pollution index has increased almost to two times, from which it can be seen that this project has made evident contribution to the growing concentration of airborne particulate.
Through the introduction of the compartment model, we made further simulation of the diffusion of airborne particulates in Chengdu from which it can be concluded that through the adjustment of various measures before and after the construction, the level of airborne particulates can be reduced to one fourth of the original one within the last five months so as to make the air quality of Chengdu reach a good standard. Combined the results of survey and the emprical literatures, this paper applies the improved FAHP to select the optimal prevention scheme, which involves eight methods: regular manual watering, regular ground sweeping, further promotion of traffic restrictions, prohibition of construction within a certain wind speed, employment of green building materials, slower speed of traffic, better tree planting and grass growing around construction site and finally intensity of local supervision. Not only can this scheme effectively cut down the airborne particulates, but also can coordinate with economic benefit to provide a new way for future major urban constructions.
Based on the previous articles, this paper makes an integrated assessment that the optimal scheme can at least reduce the emission loads of airborne particulate of about 9417.66t during the renovation project to greatly reduce the airborne particulates around the whole city. Therefore, this scheme deserves further promotion.
References
- Chen, F., Jiang, X., Tang, F., Bian, Y., (2012), Application of AHP and GIS in evaluation of agricultural soil heavy metals pollution[J], Environmental Pollution & Control, 34(07), 6-8+14.
- Erkan, T.E., Can, G.F., Turan, E., Erkan, G.F., (2014), Selecting the best warehouse data collecting system by using AHP and FAHP methods {J}, Tehnicki Vjesnik, 21(1), p8793.
- Fan, S.B., Tian, G., Cheng, S.Y., (2012), Emission characteristics of fugitive dust from unpaved roads, Environmental Science & Technology, 35(2), p106-109.
- Huo, G.Y., Wang, M., Chen, X.X., Li, Q.W., (2009), Edge detection for Medical Image Based on Improved Canny Operator, Heilongjiang Science and Technology Information, (1), p10-11.
- Kang, Q.R., Tang, J.X., Zhang, W.Z., (2010), Application of the improved FAHP Schemes Optimization, Journal of Chongqing University, 33(9), p98-103.
- Kong, X.Y., (2010), Pollution characteristic analysis and regional diffusion prediction of atmospheric heavy metal in urban areas of Chengdu, Chengdu University of Technology, Chengdu, p26-45.
- Qi, L.B., (2006), Statistics analysis and fuzzy comprehensive evaluation of Likert scale, Shandong Science, 19(2), p13-18.
- Qin, L.P., (2010), Analysis of effect on sprinkler spray control secondary dust in Linfen City, Shanxi Energy and Conservation, (3), p60-61.
- Tian, G., Fan, S.B., Huang, Y.H., Nie, L., Li, G., (2008), Relationship between wind velocity and PM_{10} concentration & emission flux of fugitive dust source, Environmental Science, 29(10), p2983-2986.
- Tian, G., Li, G., Qin, J.P., Fan, S.B., Huang, Y.H., Nie, L., (2009), Influence of traffic restriction on road and construction fugitive dust, Environmental Science, 30(5), p1528-1532.
- Xu, Z.S., (2002), Research on compatibility and consistency of fuzzy complementary judgment matrices, Journal of PLA University of Science and Technology, 3(2), p94-96.
- Zhao, W.H., Yang, G.Y., Peng, W.W., MU, D.G., (2011), Comprehensive comments on the urban traffic environment rased on FAHPa Shanghai perspective [ J], Journal of Liaoning Normal University, 34(02), p153-156.