Evaluating sustainable indicators for urban street network: Al-Najaf network as a case study

2.1 Impact of traffic on noise

Recent World Health Organization (WHO) publications emphasize that environmental noise can be viewed as a contaminant that significantly impacts public health [12,13,14]. The rapid industrialization, commercialization, and urbanization witnessed by many developing countries in recent years has given rise to the steady increase in the environmental noise climate. Urbanization has been linked to noise pollution; enduring this degree of noise on a regular basis in an urban setting can have negative physical, physiological, and psychological repercussions that frequently do not show up right away [15]. Road traffic noise has a significant impact on the ambient noise climate because it creates a continuous sound that varies hourly in an unpredictable pattern with the passing of individual cars [16]. As a result, road traffic noise is now considered by the public and policymakers to be a critical issue. One of the most used noise indicators is the weighted equivalent continuous sound pressure level over a certain day or during a 24 h period. Regulations and evaluation recommendations from the regulatory bodies make use of this indicator [17]. However, distinguishable instances of traffic noise and variations in the temporal noise profile are not picked up by conventional traffic noise indicators [18]. International environmental noise standards (Central Pollution Control Board [CPCB]) and (WHO) are 65 dBA and 70dBA, respectively Environmental Protection Agency (EPA).

In Doha, Qatar, the study by Shaaban and Abouzaid [19] evaluated traffic noise in the vicinity of a large hospital in the morning and evening. The findings showed that the noise levels were greater than the 55 dBA threshold at which significant discomfort is produced and above the WHO recommended value of 50 dBA in both the morning and evening. It also exceeds the 55 dBA daylight and 45 dBA overnight municipal noise guidelines for Qatar. The findings showed that one of the primary causes of noise was traffic.

Abdulkareem [20] investigated how Najaf City’s noise pollution indicators are evaluated. According to the author, calm areas had an 80.4 dB value, residential areas had 69.05 dB value, commercial areas had an 89.55 dB value, educational institutions had 87.1 dB value, and industrial areas had the most significant value of 108.44 dB.

Al-Duhaidahawi et al. [21] evaluated the road network in Al-Kufa City using several indices, such as noise and pollution were investigated for different roads in the city based on field data at peak traffic flow periods. They measured noise levels in all links of Al-Kufa city, which are above air quality specification by EPA [22].

According to the research by Mansour and Al-Jamil [23] on the impact of pollution and noise in a chosen urban road network, Al-Matar Street had the highest average noise level of 87 dBA – among all the streets in the city of Najaf – surpassing both WHO and CPCB limits. Al-Najaf-Kufa Street also has the highest CO concentration, which is below EPA and WHO standards at about 8 ppm.

2.2 Traffic pollution

Transportation activities play a role in contributing a third of the atmosphere’s chlorofluorocarbons (CFCs), a fifth of the atmosphere’s carbon dioxide (CO₂), and half of the atmosphere’s nitrogen oxides (NO _x ). are released by fossil fuel combustion [24]. For the general population, traffic-related air pollution is a serious issue. In large cities, automobile emissions of particulate matter (PM), carbon monoxide (CO), NO _x , and volatile organic compounds (VOC) are major contributors to air pollution. Transportation-related greenhouse gases, such as carbon dioxide (CO₂), may contribute to global warming, while air pollutants produced by traffic, such as nitrogen dioxide (NO₂) and PM, constitute a health concern [25]. Gas emission is influenced by vehicle performance, acceleration, and speed (performance rating for trucks and passenger car units). Fuel use and emissions vary with speed: the idle rate produces 50% of total emissions, the accelerating rate produces 35–40% of total emissions, and decelerating rate produces 10% of total emissions [13]. Heavy vehicles, such as trucks and buses, emit just 1/11 as much carbon dioxide (CO₂) as small cars (benzene fuel) [26].

Motor vehicles produce various harmful air emissions. Some impacts are localized. Thus, their costs are changed with emissions. Others, on the other hand, are global or regional. Therefore, the location is of less significance. Devices controlling emissions have assisted in reducing some pollutants’ emission rates. However, they do not work for all pollutants, including particles. The subsequent variables impact emission rates [27]:

• Emission control systems on older cars are less effective. Defective emission control systems produce excessive emissions.

• Higher accelerations lead to higher emission rates.

• Rates of emission are relatively higher with the presence of cold engines.

• Mile-to-mile emissions rise in stop-and-go traffic, on hills, and at high and low speeds.

The air quality index (AQI) is the function to indicate emissions as stated by EPA [22], as indicated in Table 1 [28,29]. The five primary pollutants – sulfur dioxide (SO₂), carbon monoxide (CO), NO₂, ozone (O₃), and PM₁₀ and PM_2.5 – are the foundation for the US EPA’s definition of AQI. First, concentration data from the linear interpolation algorithm and reference concentration data are used to generate each pollutant’s unique index, as shown in the equation below [30,31]:

where IP is the index for any pollutant (P), C _P is the concentration of the pollutant P, BP_HI is the breaking point that is equal to or greater than C _P, BP_LO is the breaking point that is equal to or less than C _P, I_HI is the AQI value correlating with BP_HI, I _LO is the AQI value correlating with BP_LO.

Using measured (AQ) data, Kumar et al. [32] calculated the harm caused by air pollution by evaluating the impact on health. While regional diversity is necessary for air quality control, a monitoring station represents AQ at a specific site. A ward’s overall population and data from air quality monitoring inside the ward were used to conduct a health impact evaluation. Finally, an estimation of a ward’s health costs was made using the population that was exposed.

The impact of pollution at urban intersections attributable to the transportation system was examined by Theyab et al. [33]. They attempted to investigate the relationship between traffic flow and pollutant levels at a specific Karbala City crossroads. As a result, a three-day video camera was installed to record the variations in traffic patterns every day for a full day. The findings demonstrated that at the two junctions that were selected, the CO₂ levels were higher above the permissible threshold due to subpar roads.

The greenhouse gas emissions from motor vehicles in Bangkok were calculated by Thanatrakolsri et al. [34] using an International Vehicle Emission model. The basic emission rates in the model were derived from measurements of greenhouse gas emissions made by the Automotive Emission Laboratory of Thailand. According to the findings, motor vehicle greenhouse gas emissions were 11,715.47 Gg CO₂ equivalent. Taxis accounted for 28.50 of all greenhouse gas emissions, followed by passenger vehicles, pickups, motorbikes, trucks, buses, vans, and public motorcycles at 25.94, 20.47, 8.90, 7.56, 6.28, 2.16, and 0.19%, respectively.

The results indicate that the implementation of a single mitigation measure may not be able to reduce overall greenhouse gas emissions from motor vehicles when scenarios for reducing greenhouse gas emissions are considered. These scenarios include using low-emission vehicle technology for new vehicles, reducing the use of personal motor vehicles, switching to public transport, and eliminating vehicles older than 15 years. On the other hand, a comprehensive strategy that incorporates every mitigation strategy might cut greenhouse gas emissions by 23.68%.

6.1 Flow rates

The roads were chosen within the urban road network of the city of Najaf to cover approximately the whole road network in the city. A spatial engineering database was created within the (GIS) program for road networks in Najaf Governorate, as the roads were named, and a code was given for each road as shown in Figure 1.

The traffic performance was evaluated previously using two methods: HCM 2000 and HCM 2010. Both methods indicated that most of these urban streets were congested (for more details, refer [10,11,36]. The maximum service flow rate has been indicated in Appendix (A) (Table A3).

After determining the traffic volumes for all the selected and specified roads within the study area, the analysis was conducted to calculate the flow rates for each road and calculate the proportions of the types and number of cars as indicated in Figure 2. This figure demonstrates the location of point data where the camera has been installed along the urban street.

Figure 2

Karbala Road (AN_009).

Table 3 indicates a sample of the data that was recorded and monitored on Karbala Road (AN_009) and within the street heading toward Diwaniyah Governorate, where the date, road code, and coordinates of the monitoring point on the side of the road that was near the Al-shuhada bridge were recorded, and flow rates were calculated, as shown in Table 3.

Figure 3 indicates the flow rates on Karbala Road for the street leading to Diwaniyah Governorate and the street leading to Karbala Governorate during the monitoring period from 6.35 until 9 o’clock in the morning, where the peak period appears (70 min).

Figure 3

Traffic flow rates at Karbala Road.

Upon conducting a thorough analysis of the collected data, a noticeable trend emerged, indicating elevated flow rates occurring between 7:10 AM and 8:30 AM. Concurrently, this operation in flow rates was found to have an apparent impact on the reduction in vehicle speeds, as depicted in Figure 4. At the same time, it turns out that there is an inverse relationship, as this increase in flow rates has a noticeable effect on reducing vehicle speeds, as shown in Figure 4. This relationship has the same behavior as that mentioned in Greenshield [8].

Figure 4

Speed–flow distribution in Karbala Road.

The above speed–flow relationship has indicated that behavior as mentioned in Greenshield [8,9]. According to the class of this road, see detail in HCM [8,9], the LOS is within congested level (LOS F). Furthermore, this behavior has been noticed for all these streets as indicated in Appendix (A) (Table A3).

6.2 Traffic mix

Traffic mix is indicated in Appendix A (Table A1). This table shows the traffic mix as follows:

• Private cars constitute the majority, comprising a substantial portion of the overall traffic within the study area.

• Based on the data, buses and minibuses make up roughly 1 and 5% of the total traffic, respectively. This suggests a significant deficiency in the public transport sector in the study area. It is worth noting that a substantial proportion of the buses in operation in the urban road network are owned and operated by individuals and private travel companies.

6.3 Noise generated from traffic flow

Noise pollution is typically defined as being exposed to high amounts of sound that can be harmful to people or other living organisms. Recent publications by WHO have highlighted the perception of environmental noise as a pollutant with profound effects on public health [37]. This concern is particularly directed toward exposure to traffic-related noise levels deemed hazardous to health, specifically those exceeding 55 dB [38]. In this context, noise measurements were conducted in the study area during daylight hours at midblock sections using a noise level meter. These readings were then evaluated against the international benchmarks set by the CPCB and WHO, as detailed in Appendix A.

Data and measurements of noise levels in the urban road network of the city of Najaf indicate that they have reached levels higher than the standard levels approved by the Public Health Organization and approved international standards, and this is an unhealthy indicator, as indicated in Figure 5.

Figure 5

Noise levels in the study area.

6.4 Traffic pollution emissions

Traffic emissions were monitored in nine locations within the study area on the urban road network of the city of Najaf, as indicated in Figure 6, in the morning and evening. Advanced Sense Environmental Test Meter, AQDPI and HUMA-I (HI-150) devices were used to detect emissions like CO, NO₂, sulfur dioxide (SO₂), CO₂, formaldehyde (HCHO), total volatile organic compounds (TVOC), PM_2.5 and PM₁₀, temperature, and AQ Index (AQI) were also measured as indicated in Table 4.

Figure 6

Traffic emissions in the study area.

The results of monitoring traffic emissions in the study area showed that there is an increase in the values of these emissions above the approved international and standard limits, as indicated in Table 5. This is an unhealth indicator for the city.

In the light of above, the level of noise is approximately unhealthy for some roads. Therefore, this study recommends providing green zones around the major roads in the city. The major roads are AN_008, AN_009, and AN_010, and they have enough right of way to provide a strip of green zone along these roads. These strips of green zone will mitigate the level of pollution.