Effects of urbanization, scale, and geography on air pollution in India

Abstract Indian cities have recently emerged as some of the locations with the worst air pollution on Earth. However, the sources of these increases and potential mitigation paths are complex, involving an interplay between urbanization, economic growth, energy systems and environmental management. Here, we analyse an extensive set of satellite and ground-level air quality monitoring data sets to create a detailed examination of geographical and temporal patterns of $$N{O}_{2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>N</mml:mi> <mml:msub> <mml:mrow> <mml:mi>O</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> pollution across all districts and most large cities in India. For the period 2005–19, we find that urbanization has an inverse relationship with $$N{O}_{2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>N</mml:mi> <mml:msub> <mml:mrow> <mml:mi>O</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> increase, but at the same time, the most urbanized districts also have the highest $$N{O}_{2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>N</mml:mi> <mml:msub> <mml:mrow> <mml:mi>O</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> burden. Districts at intermediate levels of urbanization ( $$30-40 \%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>30</mml:mn> <mml:mo>−</mml:mo> <mml:mn>40</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> ) show both high levels of $$N{O}_{2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>N</mml:mi> <mml:msub> <mml:mrow> <mml:mi>O</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> and the largest increases in $$N{O}_{2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>N</mml:mi> <mml:msub> <mml:mrow> <mml:mi>O</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> pollution. Our analysis also reveals that the region of the Indo-Gangetic Plain (IGP) is of particular concern both in terms of high absolute levels of $$N{O}_{2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>N</mml:mi> <mml:msub> <mml:mrow> <mml:mi>O</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> and relative $$N{O}_{2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>N</mml:mi> <mml:msub> <mml:mrow> <mml:mi>O</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> increase over the period from 2005 to 2019. Studying $$P{M}_{10}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>P</mml:mi> <mml:msub> <mml:mrow> <mml:mi>M</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>10</mml:mn> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> levels across 106 cities, we find that they exceed national air quality standards in over 80% of cases, with the problem also being particularly acute in IGP’s urban areas. Scaling analysis reveals that $$N{O}_{2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>N</mml:mi> <mml:msub> <mml:mrow> <mml:mi>O</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> concentrations increase super-linearly with population in cities in the IGP, in contrast to the sublinear scaling observed for other cities. This implies that larger cities in the IGP are characterized by both higher $$N{O}_{2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/Ma