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The economic costs of pollution are large, but uncertain. This book, from some of the leading analysts in the field, provides a much-needed accessible description of the methods for calculating externalities, the results and their policy implications. The authors clearly address the relevant uncertainties and ethical underpinnings, bringing a balanced approach to complex issues that are so often distorted by vested interests.-- Source other than Library of Congress.

The development of the green economy is universally recognized as a solution to natural resource shortages and environmental pollution. When exploring and developing a green economy, it is important to study the relationships between the environment and economic development. As opposed to descriptive and qualitative research without modeling or based on environmental Kuznets curves, quantitative relationships between environmental protection and economic development must be identified for exploration and practice. In this paper, we used the generalized additive model (GAM) regression method to identify relationships between atmospheric pollutants (e.g., NO2, SO2 and CO) from remote sensing and in situ measurements and their driving effectors, including meteorology and economic indicators. Three representative cities in the Anhui province, such as Hefei (technology-based industry), Tongling (resource-based industry) and Huangshan (tourism-based industry), were studied from 2016 to 2020. After eliminating the influence of meteorological factors, the relationship between air quality indexes and industrial production in the target cities was clearly observed. Taking Hefei, for example, when the normalized output of chemical products increases by one unit, the effect on atmospheric NO2 content increases by about 20%. When the normalized output of chemical product increases by one unit, the effect on atmospheric SO2 content increases by about 10%. When chemical and steel product outputs increase by one unit, the effect on atmospheric CO content increases by 25% and 20%, respectively. These results can help different cities predict local economic development trends varying by the changes in air quality and adjust local industrial structure.

Climate change mitigation measures can yield substantial air quality improvements while emerging clean air measures in developing countries can also lead to CO 2 emission mitigation co-benefits by affecting the local energy system. Here, we evaluate the effect of China’s stringent clean air actions on its energy use and CO 2 emissions from 2013-2020. We find that widespread phase-out and upgrades of outdated, polluting, and inefficient combustion facilities during clean air actions have promoted the transformation of the country’s energy system. The co-benefits of China’s clean air measures far outweigh the additional CO 2 emissions of end-of-pipe devices, realizing a net accumulative reduction of 2.43 Gt CO 2 from 2013-2020, exceeding the accumulated CO 2 emission increase in China (2.03 Gt CO 2 ) during the same period. Our study indicates that China’s efforts to tackle air pollution induce considerable climate benefit, and measures with remarkable CO 2 reduction co-benefits deserve further attention in future policy design. China’s clean air action stimulated a net accumulative reduction of 2.43 Gt CO 2 emission from 2013-2020. Phase-out and upgrades of outdated, polluting, and inefficient combustion facilities have promoted the transition of the country’s energy system.

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.

Poor air quality is currently responsible for large impacts on human health across the world. In addition, the air pollutants ozone (O3) and particulate matter less than 2.5 µm in diameter (PM2.5) are also radiatively active in the atmosphere and can influence Earth's climate. It is important to understand the effect of air quality and climate mitigation measures over the historical period and in different future scenarios to ascertain any impacts from air pollutants on both climate and human health. The Coupled Model Intercomparison Project Phase 6 (CMIP6) presents an opportunity to analyse the change in air pollutants simulated by the current generation of climate and Earth system models that include a representation of chemistry and aerosols (particulate matter). The shared socio-economic pathways (SSPs) used within CMIP6 encompass a wide range of trajectories in precursor emissions and climate change, allowing for an improved analysis of future changes to air pollutants. Firstly, we conduct an evaluation of the available CMIP6 models against surface observations of O3 and PM2.5. CMIP6 models consistently overestimate observed surface O3 concentrations across most regions and in most seasons by up to 16 ppb, with a large diversity in simulated values over Northern Hemisphere continental regions. Conversely, observed surface PM2.5 concentrations are consistently underestimated in CMIP6 models by up to 10 µg m−3, particularly for the Northern Hemisphere winter months, with the largest model diversity near natural emission source regions. The biases in CMIP6 models when compared to observations of O3 and PM2.5 are similar to those found in previous studies. Over the historical period (1850–2014) large increases in both surface O3 and PM2.5 are simulated by the CMIP6 models across all regions, particularly over the mid to late 20th century, when anthropogenic emissions increase markedly. Large regional historical changes are simulated for both pollutants across East and South Asia with an annual mean increase of up to 40 ppb for O3 and 12 µg m−3 for PM2.5. In future scenarios containing strong air quality and climate mitigation measures (ssp126), annual mean concentrations of air pollutants are substantially reduced across all regions by up to 15 ppb for O3 and 12 µg m−3 for PM2.5. However, for scenarios that encompass weak action on mitigating climate and reducing air pollutant emissions (ssp370), annual mean increases in both surface O3 (up 10 ppb) and PM2.5 (up to 8 µg m−3) are simulated across most regions, although, for regions like North America and Europe small reductions in PM2.5 are simulated due to the regional reduction in precursor emissions in this scenario. A comparison of simulated regional changes in both surface O3 and PM2.5 from individual CMIP6 models highlights important regional differences due to the simulated interaction of aerosols, chemistry, climate and natural emission sources within models. The projection of regional air pollutant concentrations from the latest climate and Earth system models used within CMIP6 shows that the particular future trajectory of climate and air quality mitigation measures could have important consequences for regional air quality, human health and near-term climate. Differences between individual models emphasise the importance of understanding how future Earth system feedbacks influence natural emission sources, e.g. response of biogenic emissions under climate change.

Urban aerosol measurements are necessary to establish associations between air pollution and human health outcomes and to evaluate the efficacy of air quality legislation and emissions standards. The measurement of urban aerosol particle size distributions (PSDs) is of particular importance as they enable characterization of size-dependent processes that govern a particle's transport, transformation, and fate in the urban atmosphere. PSDs also improve our ability to link air pollution to health effects through evaluation of particle deposition in the respiratory system and inhalation toxicity. To inform future measurements of urban aerosol observations, this paper reviews and critically analyzes the current state of knowledge on urban aerosol PSD measurements by synthesizing 737 PSD observations made between 1998 to 2017 in 114 cities in 43 countries around the globe. Significant variations in the shape and magnitude of urban aerosol number and mass PSDs were identified among different geographical regions. In general, number PSDs in Europe (EU) and North America, Australia, and New Zealand (NAAN) are dominated by nucleation- and Aitken-mode particles. PSDs in Central, South, and Southeast Asia (CSSA) and East Asia (EA) are shifted to larger sizes, with a meaningful contribution from the accumulation mode. Urban mass PSDs are typically bimodal, presenting a dominant mode in the accumulation mode and a secondary mode in the coarse mode. Most PSD observations published in the literature are short-term, with only 14 % providing data for longer than 6 months. There is a paucity of PSDs measured in Africa (AF), CSSA, Latin America (LA), and West Asia (WA), demonstrating the need for long-term aerosol measurements across wide size ranges in many cities around the globe. Geographical variations in urban aerosol effective densities were also reviewed. Size-resolved urban aerosol effective density functions from 3 to 10 000 nm were established for different geographical regions and intra-city sampling locations in order to accurately translate number PSDs to mass PSDs, with significant variations observed between near-road and urban background sites. The results of this study demonstrate that global initiatives are urgently needed to develop infrastructure for routine and long-term monitoring of urban aerosol PSDs spanning the nucleation to coarse mode. Doing so will advance our understanding of spatiotemporal trends in urban PSDs throughout the world and provide a foundation to more reliably elucidate the impact of urban aerosols on atmospheric processes, human health, and climate.

The COVID-19 pandemic lockdowns led to a sharp drop in socio-economic activities in China in 2020, including reductions in fossil fuel use, industry productions, and traffic volumes. The short-term impacts of lockdowns on China's air quality have been measured and reported, however, the changes in anthropogenic emissions have not yet been assessed quantitatively, which hinders our understanding of the causes of the air quality changes during COVID-19. Here, for the first time, we report the anthropogenic air pollutant emissions from mainland China by using a bottom-up approach based on the near-real-time data in 2020 and use the estimated emissions to simulate air quality changes with a chemical transport model. The COVID-19 lockdown was estimated to have reduced China's anthropogenic emissions substantially between January and March in 2020, with the largest reductions in February. Emissions of SO2, NOx, CO, non-methane volatile organic compounds (NMVOCs), and primary PM2.5 were estimated to have decreased by 27 %, 36 %, 28 %, 31 %, and 24 %, respectively, in February 2020 compared to the same month in 2019. The reductions in anthropogenic emissions were dominated by the industry sector for SO2 and PM2.5 and were contributed to approximately equally by the industry and transportation sectors for NOx, CO, and NMVOCs. With the spread of coronavirus controlled, China's anthropogenic emissions rebounded in April and since then returned to the comparable levels of 2019 in the second half of 2020. The provinces in China have presented nearly synchronous decline and rebound in anthropogenic emissions, while Hubei and the provinces surrounding Beijing recovered more slowly due to the extension of lockdown measures. The ambient air pollution presented much lower concentrations during the first 3 months in 2020 than in 2019 while rapidly returning to comparable levels afterward, which have been reproduced by the air quality model simulation driven by our estimated emissions. China's monthly anthropogenic emissions in 2020 can be accessed from https://doi.org/10.6084/m9.figshare.c.5214920.v2 (Zheng et al., 2021) by species, month, sector, and province.