Modeling environmental noise pollution around the 1893 educational institutions for children in Tehran to support new urban design strategies – Nature

Report on Environmental Noise Pollution in Educational Zones in Tehran

A Study in Support of Sustainable Development Goals

1.0 Introduction: The Imperative for Sustainable Urban Environments for Children

Environmental Noise Pollution (ENP) is a significant environmental risk factor that compromises global public health, directly impeding the achievement of Sustainable Development Goal 3 (Good Health and Well-being). Urban noise, primarily from transportation and commercial activities, has been shown to negatively impact the central nervous system, increasing the risk of mental and behavioral disorders, particularly in children. This vulnerability undermines Sustainable Development Goal 4 (Quality Education), as prolonged exposure to noise can impair cognitive development, literacy, attention, and memory in children during crucial developmental stages.

Children spend a significant portion of their time in educational institutions, making these environments critical zones for health and development. This report details a study designed to map ENP levels around all elementary schools and kindergartens in Tehran. By employing a Land Use Regression (LUR) model, the study aims to provide actionable data for policymakers to create healthier, more equitable learning environments, thereby contributing to the objectives of Sustainable Development Goal 11 (Sustainable Cities and Communities), which seeks to make cities inclusive, safe, resilient, and sustainable.

2.0 Methodology: Assessing Environmental Noise Pollution

2.1 Study Domain and Scope

The study was conducted in Tehran, the capital of Iran, a megacity with a daytime population exceeding 12 million. The analysis included 895 elementary schools and 998 kindergartens, which collectively serve approximately 800,000 students. Geospatial data for these institutions were processed to map their distribution across the city’s 22 districts.

2.2 Data Collection and Analysis

ENP data, measured as 10-minute equivalent noise levels (Leq10min), were collected by the Tehran Air Quality Control Company at 308 locations adjacent to schools and kindergartens. Measurements were conducted during official school hours (8:00 a.m. to 3:00 p.m.) on weekdays to accurately reflect the noise exposure experienced by children. Standard measurement protocols were followed, using a B&K2230 sound level meter positioned 1.5 meters above the ground and away from reflective surfaces.

2.3 Land Use Regression (LUR) Model Development

To predict ENP levels at all educational sites, an LUR model was developed. This statistical approach is a critical tool for evidence-based urban planning, a key component of SDG 11. The model utilized 135 spatial predictor variables extracted from eight buffer zones (radii from 25 m to 400 m) around each monitoring station. These variables were categorized as follows:

• Green Spaces
• Highways and Roads (Primary, Secondary)
• Land Use (Residential, Commercial, Industrial)
• Transportation Hubs (Terminals, Bus/Taxi Stations, Parking Lots)
• Fuel Stations
• Population Density

The final model was validated using leave-one-out cross-validation (LOOCV) and assessed for multicollinearity using the variance inflation factor (VIF).

3.0 Findings: Noise Pollution and Its Implications for Sustainable Development Goals

3.1 Spatial Distribution of ENP and Urban Inequality

The study revealed significant spatial disparities in ENP exposure across Tehran, highlighting a critical challenge to Sustainable Development Goal 10 (Reduced Inequalities). The highest average noise levels, ranging from 65.1 dB(A) to 85 dB(A), were concentrated in the central, southern, and southeastern districts, which typically have a lower socio-economic status. In contrast, the northern districts experienced lower ENP.

Critically, the average ENP in 19 of the 22 districts exceeded the 65 dB(A) standard for commercial areas, let alone the 55 dB(A) standard for residential zones. This widespread exposure to excessive noise directly threatens the health and educational outcomes of a vast majority of Tehran’s children, undermining both SDG 3 and SDG 4.

The LUR model estimated the ENP exposure for all 1,893 institutions:

• 36% of institutions are exposed to ENP in the range of 70.1–75 dB(A).
• 30% are exposed to ENP between 65–70 dB(A).
• 13% are exposed to ENP levels greater than 75 dB(A).
• Only 4% are located in areas with ENP below 60 dB(A).

3.2 LUR Model Results: Identifying Key Urban Planning Levers

The final LUR model demonstrated strong predictive performance (R² = 0.70, Adjusted R² = 0.65, LOOCV R² = 0.59) and identified seven significant spatial predictors of ENP. These findings provide actionable insights for urban planning aligned with SDG 11.

1. Factors Supporting Noise Reduction (Negative Correlation):
   • Area of Green Space (within 350m): Greater green space is associated with lower noise levels, supporting SDG 11.7 (access to green spaces) and SDG 15 (Life on Land).
   • Distance to Nearest Terminal, Primary Road, and Highway: Increased distance from major transport infrastructure significantly reduces noise.
2. Factors Contributing to Noise Increase (Positive Correlation):
   • Length of Secondary Roads (within 100m): Denser local road networks increase noise.
   • Area of Commercial Parcels (within 100m): Proximity to commercial activity is a major noise source.
   • Distance to Nearest Military Zone: This variable served as a proxy for proximity to denser, high-traffic urban centers.

4.0 Discussion: Pathways to Achieving SDGs Through Urban Redesign

4.1 Urban Morphology and Socio-Economic Disparities

The concentration of high ENP levels in central and southern Tehran is attributable to a combination of dense urban infrastructure, high traffic volumes from daily commuters, and socio-economic factors. This spatial inequity means that children from lower-income communities are disproportionately burdened by noise pollution, a clear violation of the principles of SDG 10 (Reduced Inequalities) and the universal health coverage targets of SDG 3.

4.2 Policy Implications for Sustainable and Healthy Cities

The findings strongly advocate for the adoption of innovative urban planning strategies to mitigate ENP and advance multiple SDGs.

• Infrastructure Modification: Redesigning urban infrastructure is a direct strategy for achieving SDG 11. Implementing models such as the “Superblock” or “15-Minute City,” which prioritize pedestrian and green transport over private vehicles, can simultaneously reduce noise, lower air pollution, and promote physical activity, contributing to SDG 3, SDG 11, and SDG 13 (Climate Action).
• Expansion of Green Spaces: Increasing the area of green space around schools is a proven method for noise attenuation. This aligns with SDG 11.7, which calls for universal access to safe, inclusive, and accessible green and public spaces.
• Strategic Siting of New Schools: Future educational institutions must be located away from major noise sources like highways, terminals, and commercial zones. This proactive approach is essential to ensure that learning environments are conducive to quality education, as mandated by SDG 4.
• Immediate Mitigation Measures: For existing schools in high-noise areas, practical interventions are necessary. The study found that only 10% of institutions had double-glazed windows. Mandating their installation is a cost-effective measure to protect children’s health (SDG 3).

5.0 Conclusion: A Call to Action for Child-Centric Sustainable Urban Planning

This study confirms that children in Tehran’s elementary schools and kindergartens are exposed to harmful levels of environmental noise pollution, with significant inequalities observed across the city. The LUR model successfully identified key urban predictors, including proximity to green spaces, transportation infrastructure, and commercial areas, that influence ENP levels.

Addressing ENP is not merely an environmental issue; it is a critical component of sustainable development. The evidence presented in this report provides a clear mandate for policymakers to implement new urban design strategies. By prioritizing the reduction of noise pollution in and around educational zones, Tehran can make substantial progress toward achieving SDG 3 (Good Health and Well-being), SDG 4 (Quality Education), SDG 10 (Reduced Inequalities), and SDG 11 (Sustainable Cities and Communities), ensuring a healthier and more equitable future for its children.

1. Which SDGs are addressed or connected to the issues highlighted in the article?

SDG 3: Good Health and Well-being

• The article directly links Environmental Noise Pollution (ENP) to significant health risks. It states that ENP is a “leading risk factor, threatening the health of millions globally” and increases the “risk of mental health issues, such as depression, anxiety, suicidal tendencies, and behavioral disorders, particularly in children and adolescents.” The mention of the World Health Organization’s report that ENP contributes to a loss of “over 1.6 million healthy life years annually” further solidifies its connection to public health and well-being.

SDG 4: Quality Education

• The study’s focus is on noise pollution around elementary schools and kindergartens. The article highlights that “prolonged exposure to such ERF [Environmental Risk Factors] can impede cognitive development” and has a “detrimental impact on children’s cognitive processes, including literacy, attention, mathematics, and memory.” This directly relates to the quality of the learning environment, which is crucial for achieving educational goals.

SDG 11: Sustainable Cities and Communities

• The entire study is framed within an urban context (Tehran) and analyzes the relationship between land use, urban infrastructure, and pollution. It discusses how factors like “green space area,” “primary roads, and highways,” and “commercial parcels” influence noise levels. The conclusion calls for “new urban design strategies” and adopting “innovative urban planning strategies” to create healthier urban environments, which is the core of SDG 11.

SDG 10: Reduced Inequalities

• The article identifies a “clear spatial inequity in ENP” across Tehran. It notes that “northern districts of Tehran experience lower ENP compared to the central and southern districts,” attributing this to differences in “socio-economic status (SES).” This finding points to an unequal distribution of environmental burdens within the city, directly connecting the issue to the goal of reducing inequalities.

2. What specific targets under those SDGs can be identified based on the article’s content?

SDG 3: Good Health and Well-being

1. Target 3.4: By 2030, reduce by one third premature mortality from non-communicable diseases through prevention and treatment and promote mental health and well-being. The article’s focus on noise pollution’s impact on mental health (depression, anxiety) and its role as a risk factor for other health issues aligns with this target’s emphasis on prevention and mental well-being.
2. Target 3.9: By 2030, substantially reduce the number of deaths and illnesses from hazardous chemicals and air, water and soil pollution and contamination. Environmental noise is a form of pollution that, as the article states, leads to a significant loss of healthy life years, making this target highly relevant.

SDG 4: Quality Education

1. Target 4.a: Build and upgrade education facilities that are child, disability and gender sensitive and provide safe, non-violent, inclusive and effective learning environments for all. The study’s finding that the vast majority of schools are exposed to high noise levels that impede cognitive development directly addresses the need for “safe and effective learning environments.”

SDG 11: Sustainable Cities and Communities

1. Target 11.6: By 2030, reduce the adverse per capita environmental impact of cities, including by paying special attention to air quality and municipal and other waste management. Noise is a major adverse environmental impact in cities. The study’s mapping of ENP is a direct effort to measure and provide data to manage this impact.
2. Target 11.7: By 2030, provide universal access to safe, inclusive and accessible, green and public spaces… The article identifies “green space area” as a key factor that has a “negative effect on ENP,” meaning it reduces noise. This supports the goal of expanding green spaces to improve urban environmental quality.

SDG 10: Reduced Inequalities

1. Target 10.2: By 2030, empower and promote the social, economic and political inclusion of all, irrespective of… economic or other status. The article’s discovery of “spatial inequity” where lower socio-economic districts suffer from higher noise pollution highlights an environmental inequality that hinders the inclusive development of all communities.

3. Are there any indicators mentioned or implied in the article that can be used to measure progress towards the identified targets?

Indicators for SDG 3 & SDG 11 (Targets 3.9 & 11.6)

• Environmental Noise Pollution (ENP) levels in decibels (dB(A)): The primary indicator used throughout the article is the measured and modeled noise level. Specific data points like “highest ENP values—ranging from 65.1 dB(A) to 85 dB(A)” and average levels in different districts (e.g., “district 22 with 58.7 dB(A)”) serve as direct metrics for pollution. Progress would be measured by a reduction in these values over time.

Indicator for SDG 4 (Target 4.a)

• Percentage of educational institutions exposed to noise levels above a recommended threshold: The article provides this data explicitly, stating that “approximately 36%… are exposed to ENP in the range of 70.1–75 dB(A)” and “only 4% of these institutions are located in areas with ENP

Indicator for SDG 11 (Target 11.7)

• Area of green space in proximity to schools and kindergartens: The study identifies “green space area within a 350-meter buffer” as a significant variable that reduces noise. This can be used as a planning indicator to measure access to green space, with an increase in this metric indicating progress.

Indicator for SDG 10 (Target 10.2)

• Disaggregated data on ENP levels by urban district and socio-economic status: The article provides a clear indicator of inequality by comparing ENP levels between “northern districts of Tehran” (higher SES, lower noise) and “the central and southern districts” (lower SES, higher noise). Tracking the gap in noise exposure between these areas would be a key indicator of progress in reducing environmental inequality.

4. Create a table with three columns titled ‘SDGs, Targets and Indicators” to present the findings from analyzing the article.

Source: nature.com