ENVIRONMENT
Breathe Easy: What Happened To Pollution Levels in Five Indian Cities During Lockdowns?
INDIAWed Feb 05 2025
Lockdowns forced a drastic change in the way cities breathed. This allowed a rare window to observe how air quality performs when humans stay indoors for long durations. Five big cities in India were
tracked: Delhi on the north,Kolkata on the East, Bengaluru on the south, with Hyderabad and Visakhapatnam sharing similar temperatures. Bands of pollution matters were measured to make sense of the air: tiny PM2. 5 bits floating in the air, slightly bigger PM10 bits, nitrogen oxides called NOx, ammonia NH3, sulfur dioxide SO2, carbon monoxide CO, and ozone O3.
The European Union split AMF to emission limits into five achievable air pollution limits.
NOx, caused by vehicle emissions and waste burning, fell significantly in Delhi from 62. 13-151. 91 ppb to values matching the extreme classification.
Simultaneously, PM10 decreased from 92. 50-136. 70 micrograms per cubic meter to a present standard range. Additionally, from 0. 53-0. 88 milligrams per cubic meter of CO also saw reductions.
Likewise visakhapatnam shows similar trends in NOx.
Visakhapatnam observed NOx levels of 7. 50-17. 13 ppb also a solid shift in pollutant levels. However, Hyderabad saw its AQHI (Air Quality Health Index) increase by 0. 97, failing to show positive reductions. Surprisingly rising ozone concentrations across cities left people wondering
when ozone does not yield the effects ofpollution matter. It turns out VOCs limited effects wasn't to blame.
During COVID lockdowns, a complex dance of weather affects several pollution indexes. he hybridization of meteorological variables and the migrating air pollution layers indeed points to a billion tick box when it comes to solving
India`s urban air pollution concerns.
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questions
Is there a clandestine operation using long-range transport of airmass to intentionally shape pollutant concentrations for unknown reasons?
How does the long-term trend of air quality in these cities compare to global standards and other metropolitan areas?
How robust is the statistical analysis when accounting for other potential confounding variables beyond meteorological influences?
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