There is no doubt that the global spread of COVID-19
represents the defining crisis of the last decade. Governments around the world
have scrambled to try to reduce person-to-person spread and deal with pressures
on public health infrastructure. Regions with community spread have almost
universally faced restrictions on travel, business and social activities. These
restrictions are designed to reduce the exponential spread of COVID-19 (that
is, to flatten the curve), these restrictions will also have a large number of
other economic, social and environmental repercussions. Here, I ask a simple
question: Has reductions in economic activity and movement caused by
governmental responses to COVID-19 improved air quality in cities? I compare
February 2019 and 2020 air quality measures and show that six cities that were
impacted early by government restrictions in response to COVID-19 show
consistent declines in five of six major air pollutants compared to cities that
were impacted later (the text in this post has been modified from
Cadotte 2020).
One of the most pernicious and inevitable consequences of
urbanization and industrialization is the release of air pollutants. The
WorldHealth Organization (WHO) estimates that about 90% of urban residents
experience air pollution that exceeds WHO guidelines and that air pollution is
responsible for more than four million premature deaths annually
(World Health Organization 2018). Air quality
is adversely affected by the aerosol release of a number of chemical compounds
from agriculture, manufacturing, combustion engines and garbage incineration,
and is usually assessed by measuring the atmospheric concentrations of six key
pollutants: fine particulate matter (PM
2.5), course particulate
matter (PM
10), ground-level ozone (O
3), nitrogen dioxide
(NO
2), sulfur dioxide (SO
2), and carbon monoxide (CO). These pollutants have a number of serious human health impacts (Table 1). Reducing inputs of
these pollutants into urban areas requires a combination of technological
advancement and behaviour change that can be stimulated by governmental
regulations and incentives.
Table 1: The six commonly measured air pollutants in cities and their human health impacts.
Alterations of human, transport and industrial activity are
usually the result of long-term economic and behavioural change and difficult to
legislate under normal situations. However, the recent emergence of the global
COVID-19 pandemic has had clear epidemiological impacts with, as of March 25,
2020, almost half a million confirmed infections and close to 20,000 deaths
(World Health Organization 2020). This pandemic
has resulted in emergency measures attempting to reduce transmission rates that
limit activity, movement and commerce in jurisdictions around the world. While
these emergency measures are critically important to limit the spread and
impact of the coronavirus, they also provide a glimpse into how governmental
calls for behavioural change can alter air pollution levels in cities.
Early evidence reveals that pollution levels have dropped in places that have undergone COVID-19 shutdowns. As Marshall Burke showed in a
blog post, PM
2.5 and PM
10, levels are lower than expected in parts of China. Here I
examine January and February 2020 AQI levels for the six pollutants in Wuhan to what would
be expected under normal circumstances. I further compare the change in
February air pollution levels over the past two years in six cities that
instituted emergency measures by the end of February (early impacted cities) to
11 cities that did not declare states of emergency until March (later impacted
cities) using freely available air monitoring data
(World Air Quality Index Project 2020) -see Table 2 for a list of cities.
Table 2: The
eleven cities used in this analysis, the month
that emergency measures were enacted and two- to six-year AQI averages of the
pollutants
|
City-data come from monitoring agencies listed at the end of this post |
Wuhan, China was the epicenter for the December 2019
emergence and the first person-to-person spread of the novel coronavirus.
In response, authorities initiated a series
drastic measures limiting human movement and activity in Wuhan and large parts
of Hubei province by the end of January. Three air pollutants: PM
2.5,
PM
10 and NO
2 all showed substantial January and February
declines in
Air Quality Index (AQI)
(U.S.Environmental Protection Agency 2014) values over 2019 levels for those
months and what would be expected from long-term trends (Fig. 1). These
long-term declining air pollution trends do reveal that
China’s recentpollution reduction and mitigation efforts are steadily paying off, but the
government-enforced restrictions further reduced pollution levels. The expected
air pollution values predicted by temporal trends (red dashed lines in Fig. 1)
are all substantially higher than the observed levels, with observed values
being between 13.85% lower than expected for January PM
2.5 and
33.93% lower for January NO
2. Further, the reductions in the
pollutants shown in Fig. 1 increased the number of days where pollutant
concentrations were categorized as ‘good’ (0
< AQI
< 50) or
‘moderate’ (51
< AQI
< 100) according to the AQI. The three
other pollutants: SO
2, O
3 and CO, all showed
idiosyncratic or non-significant changes, mostly because their levels have
already reduced significantly over time or appear quite variable (Fig. 2).
|
Fig. 1. Temporal
patterns of Air Quality Index (AQI) PM2.5, PM10 and NO2
values in Wuhan, China. Both January and February, 2020 values show significant
declines compared to 2019 levels and to that predicted from long-term trends
(red dashed line).
|
|
Fig. 2. Temporal patterns of Air Quality Index
(AQI) SO2, O3 and CO values in Wuhan, China.
|
Once COVID-19 moved to other jurisdictions, and
confirmations of community spread emerged in February 2020, emergency measures,
like those in Hubei province, were instituted to limit human movement and
interaction. The cities subjected to February restrictions include, in addition
to Wuhan, Hong Kong, Kyoto, Milan, Seoul and Shanghai, and the AQI values from
these cities were compared to other cities that did not see the impacts of the novel
coronavirus or have emergency restrictions in place until well into March.
Log-response ratios between the air concentrations of pollutants observed in
February 2020 to those from February 2019 reveal that all air pollutants except
O3 show a decline in the 2020 values for the early impacted cities
(Fig. 3). For later impacted cities, there is no overall trend in changes in
the concentrations of pollutants between 2020 and 2019 and the individual
cities in this group showed less consistency in the differences between years
(Fig. 3).
|
Fig. 3. Log response ratios for Air Quality Index (AQI) PM2.5,
PM10, NO2, O3, SO2 and CO values
between February 2019 and February 2020 values. Negative values indicate a
decline in 2020. The green symbols indicate values from an assortment of cities
that did not have emergency measures in place until March, 2020 (later impacted
cities) and orange symbols are for cities that were impacted by the end of
February.
|
These results indicate consistent air pollution reduction in
cities impacted early by the spread of the novel coronavirus. However, the
analyses presented here require further investigation as governments
increasingly restrict activity world-wide, and some are discussing the
possibility of prematurely lifting restrictions in order to spur economic
growth. Further, the data analyzed here present point estimates of air quality but
air pollution impacts are not homogeneous through urban landscapes and is
influenced by spatial variation in industrial activities and transportation (Adams & Kanaroglou 2016). Thus, as higher
resolution spatial air pollution data become available, it would be valuable to
see how reduced activity affects air quality in different parts of cities.
This analysis of early data indicates that governmental
policies that directly reduce human activity, commercial demand and transportation
can effectively and quickly reduce urban air pollution. While the COVID-19
pandemic represents a serious risk for health and wellbeing of populations
globally, especially those living in high density urban areas, the impacts of
air pollution are equally consequential. If governments are willing to expend
trillions of dollars in direct funding and indirect economic costs to combat
this disease, then why do these same governments permit or even subsidize
activities that emit air pollution? Maybe the lessons learned with
COVID-19 can serve as the impetus for further action. Perhaps mandating changes to economic or
transportation activity or investing in clean technology would better protect
human health from the effects of air pollution.
Cited sources
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Mapping real-time air pollution health risk for environmental management:
Combining mobile and stationary air pollution monitoring with neural network
models. Journal of environmental
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Cadotte, M. W. (2020) Early evidence that COVID-19 government policies reduce urban air pollution. Retrieved from eartharxiv.org/nhgj3
Cesaroni, G., Forastiere, F., Stafoggia,
M., Andersen, Z.J., Badaloni, C., Beelen, R., Caracciolo, B., de Faire, U.,
Erbel, R. & Eriksen, K.T. (2014) Long term exposure to ambient air
pollution and incidence of acute coronary events: prospective cohort study and
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Fann, N., Lamson, A.D., Anenberg, S.C.,
Wesson, K., Risley, D. &Hubbell, B.J. (2012) Estimating the National
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City air quality monitoring agencies:
1 Division of Air Quality Data, Air Quality and
Noise Management Bureau, Pollution Control Department, Thailand
(http://aqmthai.com).
2 Delhi Pollution Control Committee
(http://www.dpccairdata.com).
3 Hong Kong Environmental Protection Department
(http://www.epd.gov.hk).
4BMKG | Badan Meteorologi, Klimatologi dan
Geofisika (http://www.bmkg.go.id).
5South African Air Quality Information System
- SAAQIS (http://saaqis.environment.gov.za).
6 Japan Atmospheric
Environmental Regional Observation System (http://soramame.taiki.go.jp/).
7 UK-AIR, air quality information resource -
Defra, UK (http://uk-air.defra.gov.uk).
8 South Coast Air Quality Management District
(AQMD) (http://www.aqmd.gov/).
9 INECC - Instituto Nacional de Ecología y
Cambio Climático (http://sinaica.inecc.gob.mx).
10 Agenzia Regionale per la Protezione
dell'Ambiente della Lombardia (http://ita.arpalombardia.it).
11 CETESB - Companhia Ambiental do Estado de São
Paulo (http://cetesb.sp.gov.br).
12 Department of Public Health of the Sarajevo
Canton (http://mpz.ks.gov.ba/).
13 Air Korea Environment
Corporation (http://www.airkorea.or.kr).
14 Shanghai Environment
Monitoring Center (http://sthj.sh.gov.cn).
15 Israel Ministry of Environmental Protection (http://www.svivaaqm.net).
16 Air Quality Ontario - the Ontario Ministry of
the Environment and Climate Change (http://www.airqualityontario.com/).
17 Wuhan Environmental
Protection Bureau (http://www.whepb.gov.cn/).