Evaluating Environmental Equity in Valencia Using High‑Resolution Data

Academic abstract

Detecting vulnerable areas with poor environmental conditions is crucial for sustainable urban development. This study develops a methodology to identify vulnerable neighbourhoods at high spatial resolution by combining socio‑economic, demographic and environmental data. Applied in Valencia (Spain), the methodology uses data from 648 NO₂ passive sensors distributed across the city, combined with detailed socioeconomic and demographic indicators. Vulnerability indices for equipment, demography and socio‑economic conditions are calculated and combined into a global vulnerability index. The results highlight the most vulnerable neighbourhoods and identify a lack of direct correlation between socio‑economic vulnerability and poor air quality. Only 14.28 % of neighbourhoods were classified as both highly vulnerable and experiencing poor air quality. The findings provide valuable insights for policymakers, helping them allocate resources efficiently to improve environmental equity and living conditions. The methodology can be adapted to other urban contexts, supporting data‑driven decision‑making and targeted environmental interventions.

Supporting evidence

The study is based on extensive data collection and analysis conducted in Valencia. Key data sources include open data from the Valencia City Council and environmental data from NO₂ passive sensors deployed city‑wide. The analysis covered:

• 648 NO₂ passive sensors monitored over one year (2021) in compliance with EU Directive 2008/50/EC.
• Socioeconomic and demographic indicators such as population density, age distribution, unemployment rate, academic level and average income.
• Air quality levels classified based on percentiles, identifying areas with the highest and lowest NO₂ concentrations.

The study confirmed that areas with poor environmental conditions do not necessarily align with socio‑economically vulnerable areas. However, 14.28 % of neighbourhoods were identified as both vulnerable and exposed to poor air quality.

Key findings

1. High‑resolution data collection: Deployment of 648 NO₂ passive sensors provided detailed spatial understanding of air quality across Valencia.
2. Identification of vulnerable neighbourhoods: The most vulnerable neighbourhoods were located around the city’s periphery and were linked to poor infrastructure, high population density and low socio‑economic indicators.
3. Environmental inequity: 14.28 % of neighbourhoods experienced both high vulnerability and poor air quality, indicating significant environmental inequality.
4. Policy implications: The methodology supports targeted urban interventions, helping policymakers improve air quality and reduce socio‑economic disparities.

The study demonstrated that vulnerability and air quality levels are not strongly correlated. However, identifying the most affected areas enables targeted improvements. The methodology’s adaptability allows for its application in other cities, contributing to more equitable urban development.