China has been experiencing severe air pollution in recent decades. Although an ambient air quality monitoring network for criteria pollutants has been constructed in over 100 cities since 2013 in ...China, the temporal and spatial characteristics of some important pollutants, such as particulate matter (PM) components, remain unknown, limiting further studies investigating potential air pollution control strategies to improve air quality and associating human health outcomes with air pollution exposure. In this study, a yearlong (2013) air quality simulation using the Weather Research and Forecasting (WRF) model and the Community Multi-scale Air Quality (CMAQ) model was conducted to provide detailed temporal and spatial information of ozone (O3), total PM2.5, and chemical components. Multi-resolution Emission Inventory for China (MEIC) was used for anthropogenic emissions and observation data obtained from the national air quality monitoring network were collected to validate model performance. The model successfully reproduces the O3 and PM2.5 concentrations at most cities for most months, with model performance statistics meeting the performance criteria. However, overprediction of O3 generally occurs at low concentration range while underprediction of PM2.5 happens at low concentration range in summer. Spatially, the model has better performance in southern China than in northern China, central China, and Sichuan Basin. Strong seasonal variations of PM2.5 exist and wind speed and direction play important roles in high PM2.5 events. Secondary components have more boarder distribution than primary components. Sulfate (SO42−), nitrate (NO3−), ammonium (NH4+), and primary organic aerosol (POA) are the most important PM2.5 components. All components have the highest concentrations in winter except secondary organic aerosol (SOA). This study proves the ability of the CMAQ model to reproduce severe air pollution in China, identifies the directions where improvements are needed, and provides information for human exposure to multiple pollutants for assessing health effects.
In 2013, China's government published the Air Pollution Prevention and Control Action Plan (APPCAP) with a specific target for Beijing, which aims to reduce annual mean PM2.5 concentrations in ...Beijing to 60 µg m-3 in 2017. During 2013–2017, the air quality in Beijing was significantly improved following the implementation of various emission control measures locally and regionally, with the annual mean PM2.5 concentration decreasing from 89.5 µg m-3 in 2013 to 58 µg m-3 in 2017. As meteorological conditions were more favourable to the reduction of air pollution in 2017 than in 2013 and 2016, the real effectiveness of emission control measures on the improvement of air quality in Beijing has frequently been questioned.In this work, by combining a detailed bottom-up emission inventory over Beijing, the MEIC regional emission inventory and the WRF-CMAQ (Weather Research and Forecasting Model and Community Multiscale Air Quality) model, we attribute the improvement in Beijing's PM2.5 air quality in 2017 (compared to 2013 and 2016) to the following factors: changes in meteorological conditions, reduction of emissions from surrounding regions, and seven specific categories of local emission control measures in Beijing. We collect and summarize data related to 32 detailed control measures implemented during 2013–2017, quantify the emission reductions associated with each measure using the bottom-up local emission inventory in 2013, aggregate the measures into seven categories, and conduct a series of CMAQ simulations to quantify the contribution of different factors to the PM2.5 changes.We found that, although changes in meteorological conditions partly explain the improved PM2.5 air quality in Beijing in 2017 compared to 2013 (3.8 µg m-3, 12.1 % of total), the rapid decrease in PM2.5 concentrations in Beijing during 2013–2017 was dominated by local (20.6 µg m-3, 65.4 %) and regional (7.1 µg m-3, 22.5 %) emission reductions. The seven categories of emission control measures, i.e. coal-fired boiler control,clean fuels in the residential sector, optimize industrial structure,fugitive dust control, vehicle emission control,improved end-of-pipe control, and integrated treatment of VOCs, reduced the PM2.5 concentrations in Beijing by 5.9, 5.3, 3.2, 2.3, 1.9, 1.8, and 0.2 µg m-3, respectively, during 2013–2017. We also found that changes in meteorological conditions could explain roughly 30 % of total reduction in PM2.5 concentration during 2016–2017 with more prominent contribution in winter months (November and December). If the meteorological conditions in 2017 had remained the same as those in 2016, the annual mean PM2.5 concentrations would have increased from 58 to 63 µg m-3, exceeding the target established in the APPCAP. Despite the remarkable impacts from meteorological condition changes, local and regional emission reductions still played major roles in the PM2.5 decrease in Beijing during 2016–2017, and clean fuels in the residential sector, coal-fired boiler control, and optimize industrial structure were the three most effective local measures (contributing reductions of 2.1, 1.9, and 1.5 µg m-3, respectively). Our study confirms the effectiveness of clean air actions in Beijing and its surrounding regions and reveals that a new generation of control measures and strengthened regional joint emission control measures should be implemented for continued air quality improvement in Beijing because the major emitting sources have changed since the implementation of the clean air actions.
Air quality remains a significant environmental health challenge in India, and large sections of the population live in areas with poor ambient air quality. This article presents a summary of the ...regulatory monitoring landscape in India, and includes a discussion on measurement methods and other available government data on air pollution. Coarse particulate matter (PM
10
) concentration data from the national regulatory monitoring network for 12 years (2004–2015) were systematically analyzed to determine broad trends. Less than 1% of all PM
10
measurements (11 out of 4789) were found to meet the annual average WHO Air Quality Guideline (20 μg/m
3
), while 19% of the locations were in compliance with the Indian air quality standards for PM
10
(60 μg/m
3
). Further efforts are necessary to improve measurement coverage and quality including the use of hybrid monitoring systems, harmonized approaches for sampling and data analysis, and easier data accessibility.
The formulation of an adequate and practical Atmospheric Air Quality Management Plan at different spatial scales at local (micro), city (medium), national (macro)), and temporal (short and long term) ...is an indispensable solution to prevent the public from air pollution health risk. The air quality monitoring system provides regulatory agencies a comprehensive data of current air contaminants in a particular location. Then, air monitoring data of pollutants is processed into a dimensionless unit called the “Air Quality Index” (AQI); it serves as an information medium for the people to know the air quality health of their location and takes preventative steps accordingly (public participation). Thus, the AQI is a beneficial tool for the public, stakeholders, and regulators to understand the current state of air quality. AQI across the globe considers the number of pollutants (most of the developed countries and some developing countries considers PM
2.5
to measure the overall status of air quality being monitored), averaging time for which pollutants are measured, calculation method to compute air quality indices for each pollutant, calculation mode to aggregate the overall index, scale of an index, categories, colour coding scheme, and related descriptive terms of the pollutants. This article presents rationalized and extensive reviews of various Air Quality Index (AQI) models utilized worldwide from 1960 to 2021, comparing them based on several parameters such as types and number of pollutants (criteria or hazardous air pollutants), averaging time (long-term or short-term), calculation methods (linear or nonlinear), calculation modes single-pollutant (maximum value) or multi-pollutants (combined effect). By analysing the strengths and flaws of all the AQI models developed so far, it is recommended to develop a more reliable, extensible, and comparable AQI model to be employed as an executive tool for designing strategic pollution abatement programs to preserve public health.
We have investigated the impact of reduced emissions due to COVID-19 lockdown measures in spring 2020 on air quality in Canada’s four largest cities: Toronto, Montreal, Vancouver, and Calgary. ...Observed daily concentrations of NO
2
, PM
2.5
, and O
3
during a “pre-lockdown” period (15 February–14 March 2020) and a “lockdown” period (22 March–2 May 2020), when lockdown measures were in full force everywhere in Canada, were compared to the same periods in the previous decade (2010–2019). Higher-than-usual seasonal declines in mean daily NO
2
were observed for the pre-lockdown to lockdown periods in 2020. For PM
2.5
, Montreal was the only city with a higher-than-usual seasonal decline, whereas for O
3
all four cities remained within the previous decadal range. In order to isolate the impact of lockdown-related emission changes from other factors such as seasonal changes in meteorology and emissions and meteorological variability, two emission scenarios were performed with the GEM-MACH air quality model. The first was a Business-As-Usual (BAU) scenario with baseline emissions and the second was a more realistic simulation with estimated COVID-19 lockdown emissions. NO
2
surface concentrations for the COVID-19 emission scenario decreased by 31 to 34% on average relative to the BAU scenario in the four metropolitan areas. Lower decreases ranging from 6 to 17% were predicted for PM
2.5
. O
3
surface concentrations, on the other hand, showed increases up to a maximum of 21% close to city centers versus slight decreases over the suburbs, but O
x
(odd oxygen), like NO
2
and PM
2.5
, decreased as expected over these cities.
Ozone pollution in the Southeast US involves complex chemistry driven by emissions of anthropogenic nitrogen oxide radicals (NO(x) triple bond NO + NO2) and biogenic isoprene. Model estimates of ...surface ozone concentrations tend to be biased high in the region and this is of concern for designing effective emission control strategies to meet air quality standards. We use detailed chemical observations from the SEAC(exp 4)RS aircraft campaign in August and September 2013, interpreted with the GEOS-Chem chemical transport model at 0.25 deg x 0.3125 deg horizontal resolution, to better understand the factors controlling surface ozone in the Southeast US. We find that the National Emission Inventory (NEI) for NO(x) from the US Environmental Protection Agency (EPA) is too high. This finding is based on SEAC(exp 4)RS observations of NO(x) and its oxidation products, surface network observations of nitrate wet deposition fluxes, and OMI satellite observations of tropospheric NO2 columns. Our results indicate that NEI NO(x) emissions from mobile and industrial sources must be reduced by 30-60%, dependent on the assumption of the contribution by soil NO(x) emissions. Upper-tropospheric NO2 from lightning makes a large contribution to satellite observations of tropospheric NO2 that must be accounted for when using these data to estimate surface NO(x) emissions. We find that only half of isoprene oxidation proceeds by the high-NO(x) pathway to produce ozone; this fraction is only moderately sensitive to changes in NO(x) emissions because isoprene and NO(x) emissions are spatially segregated. GEOS-Chem with reduced NO(x) emissions provides an unbiased simulation of ozone observations from the aircraft and reproduces the observed ozone production efficiency in the boundary layer as derived from a regression of ozone and NO(x) oxidation products. However, the model is still biased high by 6 plus or minus 14 ppb relative to observed surface ozone in the Southeast US. Ozonesondes launched during midday hours show a 7 ppb ozone decrease from 1.5 km to the surface that GEOS-Chem does not capture. This bias may reflect a combination of excessive vertical mixing and net ozone production in the model boundary layer.
Tropospheric nitrogen dioxide (NO.sub.2) concentrations have declined dramatically over the United States (USA) and Europe in recent decades. Here we investigate the changes in surface and ...free-tropospheric O.sub.3 accompanied by NO.sub.2 changes over the USA and Europe in 2005-2020 by assimilating the Ozone Monitoring Instrument (OMI) and U.S. Air Quality System (AQS) and European AirBase network O.sub.3 observations. The assimilated O.sub.3 concentrations demonstrate good agreement with O.sub.3 observations. Surface O.sub.3 concentrations are 41.4, 39.5, and 39.5 ppb (parts per billion; USA) and 35.3, 32.0, and 31.6 ppb (Europe) and tropospheric O.sub.3 columns are 35.5, 37.0, and 36.8 DU (USA) and 32.8, 35.3, and 36.4 DU (Europe) in the simulations, assimilations, and observations, respectively. We find overestimated summertime surface O.sub.3 concentrations over the USA and Europe, which resulted in a surface O.sub.3 maximum in July-August in the simulations, which is in contrast to April in the observations. Furthermore, our analysis exhibits limited changes in surface O.sub.3 concentrations; i.e., they decreased by -6 % over the USA and increased by 1.5 % over Europe in 2005-2020. The surface observation-based assimilations suggest insignificant changes in tropospheric O.sub.3 columns, namely -3.0 % (USA) and 1.5 % (Europe) in 2005-2020. While the OMI-based assimilations exhibit larger decreases in tropospheric O.sub.3 columns, with -12.0 % (USA) and -15.0 % (Europe) in 2005-2020, the decreases mainly occurred in 2010-2014, corresponding to the reported slower decline in free-tropospheric NO.sub.2 since 2010. Our analysis thus suggests that there are limited impacts of the decline in local emissions on tropospheric O.sub.3 over the USA and Europe and advises more efforts to evaluate the possible contributions of natural sources and transport. The discrepancy in assimilated tropospheric O.sub.3 columns further indicates the possible uncertainties in the derived tropospheric O.sub.3 changes.
As we move through 2020, our world has been transformed by the spread of COVID-19 in many aspects. A large number of cities across the world entered “sleep mode” sequentially due to the stay-at-home ...or lockdown policies. This study exploits the impact of pandemic-induced human mobility restrictions, as the response to COVID-19 pandemic, on the urban air quality across China. Different from the “traditional” difference-in-differences analysis, a human mobility-based difference-in-differences method is used to quantify the effect of intracity mobility reductions on air quality across 325 cities in China. The model shows that the air quality index (AQI) experiences a 12.2% larger reduction in the cities with lockdown. Moreover, this reduction effect varies with different types of air pollutants (PM
2.5
, PM
10
, SO
2
, NO
2
, and CO decreased by 13.1%, 15.3%, 4%, 3.3%, and 3.3%, respectively). The heterogeneity analysis in terms of different types of cities shows that the effect is greater in northern, higher income, more industrialized cities, and more economically active cities. We also estimate the subsequent health benefits following such improvement, and the expected averted premature deaths due to air pollution declines are around 26,385 to 38,977 during the sample period. These findings illuminate a new light on the role of a policy intervention in the pollution emission, while also providing a roadmap for future research on the pollution effect of COVID-19 pandemic.
Particulate matter is a component of ambient air pollution that has been linked to millions of annual premature deaths globally
. Assessments of the chronic and acute effects of particulate matter on ...human health tend to be based on mass concentration, with particle size and composition also thought to play a part
. Oxidative potential has been suggested to be one of the many possible drivers of the acute health effects of particulate matter, but the link remains uncertain
. Studies investigating the particulate-matter components that manifest an oxidative activity have yielded conflicting results
. In consequence, there is still much to be learned about the sources of particulate matter that may control the oxidative potential concentration
. Here we use field observations and air-quality modelling to quantify the major primary and secondary sources of particulate matter and of oxidative potential in Europe. We find that secondary inorganic components, crustal material and secondary biogenic organic aerosols control the mass concentration of particulate matter. By contrast, oxidative potential concentration is associated mostly with anthropogenic sources, in particular with fine-mode secondary organic aerosols largely from residential biomass burning and coarse-mode metals from vehicular non-exhaust emissions. Our results suggest that mitigation strategies aimed at reducing the mass concentrations of particulate matter alone may not reduce the oxidative potential concentration. If the oxidative potential can be linked to major health impacts, it may be more effective to control specific sources of particulate matter rather than overall particulate mass.
The implementation of strict emission control measures in Beijing and surrounding regions during the 2015 China Victory Day Parade provided a valuable opportunity to investigate related air quality ...improvements in a megacity. We measured NH3, NO2 and PM2.5 at multiple sites in and outside Beijing and summarized concentrations of PM2.5, PM10, NO2, SO2 and CO in 291 cities across China from a national urban air quality monitoring network between August and September 2015. Consistently significant reductions of 12–35 % for NH3 and 33–59 % for NO2 in different areas of Beijing during the emission control period (referred to as the Parade Blue period) were observed compared with measurements in the pre- and post-Parade Blue periods without emission controls. Average NH3 and NO2 concentrations at sites near traffic were strongly correlated and showed positive and significant responses to traffic reduction measures, suggesting that traffic is an important source of both NH3 and NOx in urban Beijing. Daily concentrations of PM2.5 and secondary inorganic aerosol (sulfate, ammonium and nitrate) at the urban and rural sites both decreased during the Parade Blue period. During (after) the emission control period, concentrations of PM2.5, PM10, NO2, SO2 and CO from the national city-monitoring network showed the largest decrease (increase) of 34–72 % (50–214 %) in Beijing, a smaller decrease (a moderate increase) of 1–32 % (16–44 %) in emission control regions outside Beijing and an increase (decrease) of 6–16 % (−2–7 %) in non-emission-control regions of China. Integrated analysis of modelling and monitoring results demonstrated that emission control measures made a major contribution to air quality improvement in Beijing compared with a minor contribution from favourable meteorological conditions during the Parade Blue period. These results show that controls of secondary aerosol precursors (NH3, SO2 and NOx) locally and regionally are key to curbing air pollution in Beijing and probably in other mega cities worldwide.
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