Locating poor air quality in cities
Thanks to a current study with significant participation of the Helmholtz-Zentrum Hereon, the distribution of particulate matter in cities can be calculated more precisely. Within the framework of the United Nations (UN) Sustainable Development Goals (SDGs), the indicator 11.6.2 for capturing exposure to particulate matter in cities can be calculated in greater detail. Until now, the indicator recorded the particulate matter pollution in cities only coarsely. The advantages of the new method are the more precise determination of the indicator and the possibility of uniform application throughout Europe. Using Hamburg as an example, the study shows different levels of pollution according to districts, neighborhoods and even blocks. The study was recently published in the journal Remote Sensing.
People in big cities breathe bad air. Bad air that consists of particulate matter and other pollutants, which pose health risks to urban citizens. Researchers led by Dr Martin Ramacher of the Hereon Institute of Coastal Environmental Chemistry, in collaboration with the National Observatory of Athens, are now helping to make the determination of particulate matter smaller than 2.5 micrometers (PM2.5) more accurate. To do this, they used openly available EU-wide Copernicus satellite data in combination with the EPISODE-CityChem chemical transport model. The system developed at Hereon was able to model hotspots for bad air at a resolution of 100×100 square meters using Hamburg as an example. The calculated particulate matter concentrations are combined with population data and can thus simultaneously indicate areas with poor air quality and high population density. These areas are of particular interest for achieving air quality improvements. The pioneering aspect of the developed method is the combination of different satellite data, which are freely available for all of Europe, with city-scale model calculations. (Source: Hereon Press Release)
Read the complete Hereon Press Release:
==> Locating poor air quality in cities
Bailey, J., Ramacher, M.O.P., Speyer, O., Athanasopoulou, E., Karl, M., & Gerasopoulos, E. (2023): Localizing SDG 11.6.2 via Earth Observation, Modelling Applications, and Harmonised City Definitions: Policy Implications on Addressing Air Pollution. Remote Sens., 2023, 15, 1082, doi:10.3390/rs15041082
Abstract:
While Earth observation (EO) increasingly provides a multitude of solutions to address environmental issues and sustainability from the city to global scale, their operational integration into the Sustainable Development Goals (SDG) framework is still falling behind. Within this framework, SDG Indicator 11.6.2 asks countries to report the “annual mean levels of fine particulate matter (PM2.5) in cities (population-weighted)”. The official United Nations (UN) methodology entails aggregation into a single, national level value derived from regulatory air quality monitoring networks, which are non-existent or sparse in many countries. EO, including, but not limited to remote sensing, brings forth novel monitoring methods to estimate SDG Indicator 11.6.2 alongside more traditional ones, and allows for comparability and scalability in the face of varying city definitions and monitoring capacities which impact the validity and usefulness of such an indicator. Pursuing a more harmonised global approach, the H2020 SMURBS/ERA-PLANET project provides two EO-driven approaches to deliver the indicator on a more granular level across Europe. The first approach provides both city and national values for SDG Indicator 11.6.2 through exploiting the Copernicus Atmospheric Monitoring Service reanalysis data (0.1° resolution and incorporating in situ and remote sensing data) for PM2.5 values. The SDG Indicator 11.6.2 values are calculated using two objective city definitions—“functional urban area” and “urban centre”—that follow the UN sanctioned Degree of Urbanization concept, and then compared with official indicator values. In the second approach, a high-resolution city-scale chemical transport model ingests satellite-derived data and calculates SDG Indicator 11.6.2 at intra-urban scales. Both novel approaches to calculating SDG Indicator 11.6.2 using EO enable exploration of air pollution hotspots that drive the indicator as well as actual population exposure within cities, which can influence funding allocation and intervention implementation. The approaches are introduced, and their results frame a discussion around interesting policy implications, all with the aim to help move the dial beyond solely reporting on SDGs to designing the pathways to achieve the overarching targets.
