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Competing Interests: Declaration of interests Thirteen of the authors (ZA, S-CH, LJ, AM, CO, MO, JP, YP-S, DS, LB-V, MRo, MW, and HW) were compensated for their time while drafting and developing the Lancet Countdown's report. LC was supported by a grant from the National Heart, Lung, and Blood Institute of the National Institutes of Health. CD received funding from the European Research Council (FLORA, grant number 101039402). RD was supported by a grant from the High Tide Foundation and subcontracts on funds from the Wellcome Trust and US Centers for Disease Control and Prevention. GG-S received funding from the UK National Institute for Health and Care Research for the Global Health Research Group on Diet and Activity (NIHR133205, with sub-award contract number G109900-SJ1/171 with the University of Cambridge). SHG's research was supported by the National Research Foundation, Prime Minister's Office, Singapore, under its Campus for Research Excellence and Technological Enterprise programme (grant number NRF2019-THE001-0006). JJH was supported by two grants from the Wellcome Trust and a grant from the US National Science Foundation. RH, RK, and MSo acknowledge funding from Academy of Finland projects HEATCOST (grant 334798) and VFSP-WASE (grant 359421), together with EU Horizon projects FirEUrisk (grant number 101003890) and EXHAUSTION (grant number 820655). OJ was supported by grants from the National Health Medical Research Council (Heat and Health: building resilience to extreme heat in a warming world, GNT1147789); Wellcome Trust (Heat stress in ready-made garment factories in Bangladesh and the Heat inform pregnant study); and Resilience New South Wales (A new heat stress scale for general public); holds a patent for the Environmental Measurement Unit; and has received consulting fees from the National Institutes of Health. HM received funding from the Oak Foundation to support work on climate change through RealZero, is partly funded by the National Institute for Health Research's Comprehensive Biomedical Research Centre at University College London Hospitals, and received fees from Bayer Pharmaceuticals and Chiesl for sustainability consulting. JM-U was supported by grants PID2021-127107NB-I00 from Ministerio de Ciencia e Innovación (Spain) and 2021 SGR 00526 from Generalitat de Catalunya (Spain). JRo's work is supported by the Alexander von Humboldt foundation. RL, JRo, and MRo were supported by Horizon Europe through the IDAlert project (101057554) and UK Research and Innovation (reference number 10056533). RNS reports a contract with Massachusetts General Hospital. MSo and AU were supported by the Finnish Foreign Ministry project IBA-ILMA (grant number VN/13798/2023). MSp was supported by funding from the Wellcome Trust, through Our Planet Our Health (Livestock, Environment and People, award number 205212/Z/16/Z) and a Wellcome Career Development Award (Towards the full cost of diets, award number 225318/Z/22/Z). JDSh was supported by the Canadian Institutes of Health Research, the Commonwealth Fund, and the Emergency Care Research Institute and has received consulting fees from the Institute for Healthcare Research. JT was supported by the Research Council of Finland (T-Winning Spaces 2035 project), the UK Medical Research Council (PICNIC project), and the Finnish Ministry of the Environment (SEASON project). JB is employed as a consultant by the Global Climate and Health Alliance. ML received consulting fees from YarCom for advisory services and was supported by general use gifts awarded to the Center on Global Energy Policy at Columbia University, USA. JMil acknowledges consulting fees from the C40 Climate Leadership Group. CZ-C received a consultancy from the University of Alberta and was supported by contracts with her university (Universidad Peruana Cayetano Heredia), University of Leeds, WHO, and the Wellcome Trust; she was also supported by a letter of agreement between her university and the Food and Agriculture Organization's Indigenous Peoples Unit. MD was supported by the Wellcome Trust via the Complex Urban Systems for Sustainability and Health project (grants 205207/Z/16/Z and 209387/Z/17/Z). IH, S-CH, MRo, CT, and RL were supported by the Horizon Europe CATALYSE project (CATALYSE grant number 101057131, HORIZON-HLTH-2021-ENVHLTH-02, with UK Research and Innovation reference number 10041512). The work of YH, YL, DT, and QZ was supported by the National Aeronautics and Space Administration's Earth Action programme (grant number 80NSSC21K0507). AJP was supported by the Bezos Earth Fund and the Schmidt Family Foundation. ER and SD were supported by a Process-based models for climate impact attribution across sectors (PROCLIAS) grant (COST Action PROCLIAS grant CA19139), funded by European Cooperation in Science and Technology. All other authors declare no competing interests.

Competing Interests: Declaration of interests VK and OS are staff members of the WHO Regional Office for Europe. The authors alone are responsible for the views expressed in this publication and they do not necessarily represent the decisions or policies of WHO. The designations employed and the presentation of the material in this publication do not imply the expression of any opinion whatsoever on the part of WHO concerning the legal status of any country, territory, city, or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. Dotted and dashed lines on maps represent approximate border lines for which there might not yet be full agreement. AK and EV are staff members of the European Environment Agency. The views expressed in this article are solely those of the authors and its content does not necessarily represent the views or position of the European Environment Agency. All other authors declare no competing interests.

Air pollution is still one of the most severe problems in northern China, especially in the Jing-Jin-Ji region around Beijing. In recent years, China has implemented many stringent policies to address the air quality issue, including promoting energy transition toward cleaner fuels in residential sectors. But until 2020, even in the Jing-Jin-Ji region, nearly half of the rural households still use solid fuels for heating. For residents who are not covered by the clean heating campaign, we analyze five potential mitigation strategies and evaluate their environmental effects as well as the associated health benefits and costs. We estimate that substitution with electricity or gas would reduce air pollution and premature mortality more strongly, while the relatively low investment costs of implementing clean coal or biomass pellet lead to a larger benefit-cost ratio, indicating higher cost efficiency. Hence, clean coal or biomass pellet could be transitional substitution options for the less developed or remote areas which cannot afford a total transition toward electricity or natural gas in the short term.

Competing Interests: Declaration of interests 14 of the authors (MRo, MWa, LJ, MBO'H, CO, HW, CdN, HK, PL, DS, CG, ZA, MY-G, and KRvD) were compensated for their time while drafting and developing the Lancet Countdown's report. OG was supported by the EU Horizon Grant: Climate Action To Advance Healthy Societies in Europe (Project 101057131–CATALYSE), for which there is overlap of data collection and preprocessing with the materials submitted in this report. MSp was supported by the Wellcome Trust Livestock, Environment and People (grant number 205212/Z/16/Z) and Wellcome Trust (grant number 225318/Z/22/Z). JT was supported by the Academy of Finland grants for the T-Winning (grant number 353327). CD was supported by the UK Natural Environment Research Council Independent Research Fellowship (grant number NE/N01524X/1), which ended in 2021, and by the European Research Council starting grant FLORA (grant number 101039402). OJ was supported by the NHMRC Investigator Grant entitled Heat and Health: Building resilience to extreme heat in a warming world (GNT20009507), the Wellcome Trust grant Heat stress in ready-made garment factories in Bangladesh (216059/Z/19/Z), and the Resilience New South Wales grant A new heat stress scale for general public (PJ-0000850). YL was supported by funding from the National Aeronautics and Space Administration (grant number: 80NSSC21K0507) for the wildfire population exposure and fire danger indicators. TO and IH were supported by the UK Research and Innovation Engineering and Physical Sciences Research Council Centre for Research in Energy Demand Solutions (grant number EP/R035288/1). MRo was supported by funding from IDAlert project (UK Research and Innovation project reference number 10056533). KB was supported by funding from the National Institute for Health and Care Research (NIHR) Centre on Non-communicable Diseases and Environmental Change (NIHR203247). All other authors declare no competing interests. The authors alone are responsible for the views expressed in this Commission, and they do not necessarily represent the views, decisions, or policies of the institutions with which they are affiliated.

Competing Interests: Declaration of interests We declare no competing interests.

Background: Morbidity burdens from ambient air pollution are associated with market and non-market costs and are therefore important for policymaking. The estimation of morbidity burdens is based on concentration-response functions (CRFs). Most existing CRFs for short-term exposures to PM 2.5 assume a fixed risk estimate as a log-linear function over an extrapolated exposure range, based on evidence primarily from Europe and North America., Objectives: We revisit these CRFs by performing a systematic review for seven morbidity endpoints previously assessed by the World Health Organization, including data from all available regions. These endpoints include all cardiovascular hospital admission, all respiratory hospital admission, asthma hospital admission and emergency room visit, along with the outcomes that stem from morbidity, such as lost work days, respiratory restricted activity days, and child bronchitis symptom days., Methods: We estimate CRFs for each endpoint, using both a log-linear model and a nonlinear model that includes additional parameters to better fit evidence from high-exposure regions. We quantify uncertainties associated with these CRFs through randomization and Monte Carlo simulations., Results: The CRFs in this study show reduced model uncertainty compared with previous CRFs in all endpoints. The nonlinear CRFs produce more than doubled global estimates on average, depending on the endpoint. Overall, we assess that our CRFs can be used to provide policy analysis of air pollution impacts at the global scale. It is however important to note that improvement of CRFs requires observations over a wide range of conditions, and current available literature is still limited., Discussion: The higher estimates produced by the nonlinear CRFs indicates the possibility of a large underestimation in current assessments of the morbidity impacts attributable to air pollution. Further studies should be pursued to better constrain the CRFs studied here, and to better characterize the causal relationship between exposures to PM 2.5 and morbidity outcomes., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: Polluting fuels and inefficient stove technologies are still a leading cause of premature deaths worldwide, particularly in low-income and middle-income countries. Previous studies of global household air pollution (HAP) have neither considered the estimation of PM 2·5 at national level nor the corresponding attributable mortality burden. Additionally, the effects of climate and ambient air pollution on the global estimation of HAP-PM 2·5 exposure for different urban and rural settings remain largely unknown. In this study, we include climatic effects to estimate the HAP-PM 2·5 exposure from different fuel types and stove technologies in rural and urban settings separately and the related attributable global mortality burden., Methods: Bayesian hierarchical models were developed to estimate an annual average HAP-PM 2·5 personal exposure and HAP-PM 2·5 indoor concentration (including both outdoor and indoor sources). Model variables were selected from sample data in 282 peer-reviewed studies drawn and updated from the WHO Global HAP dataset. The PM 2·5 exposure coefficients from the developed model were applied to the external datasets to predict the HAP-PM 2·5 exposure globally (personal exposure in 62 countries and indoor concentration in 69 countries). Attributable mortality rate was estimated using a comparative risk assessment approach. Using weighted averages, the national level 24 h average HAP-PM 2·5 exposure due to polluting and clean fuels and related death rate per 100 000 population were estimated., Findings: In 2020, household use of polluting solid fuels for cooking and heating led to a national-level average personal exposure of 151 μg/m 3 (95% CI 133-169), with rural households having an average of 171 μg/m 3 (153-189) and urban households an average of 92 μg/m 3 (77-106). Use of clean fuels gave rise to a national-level average personal exposure of 69 μg/m 3 (62-76), with a rural average of 76 μg/m 3 (69-83) and an urban average of 49 μg/m 3 (46-53). Personal exposure-attributable premature mortality (per 100 000 population) from the use of polluting solid fuels at national level was on average 78 (95% CI 69-87), with a rural average of 82 (73-90) and an urban average of 66 (57-75). The average attributable premature mortality (per 100 000 population) from the use of clean fuels at the national level is 62 (54-70), with a rural average of 66 (58-74) and an urban average of 52 (47-57). The estimated HAP-PM 2·5 indoor concentration shows that the use of polluting solid fuels resulted in a national-level average of 412 μg/m 3 (95% CI 353-471), with a rural average of 514 μg/m 3 (446-582) and an urban average of 149 μg/m 3 (126-173). The use of clean fuels (gas and electricity) led to an average PM 2·5 indoor concentration of 135 μg/m 3 (117-153), with a rural average of 174 μg/m 3 (154-195) and an urban average of 71 μg/m 3 (63-80). Using time-weighted HAP-PM 2·5 indoor concentrations, the attributable premature death rate (per 100 000 population) from the use of polluting solid fuels at the national level is on average 78 (95% CI 72-84), the rural average being 84 (78-91) and the urban average 60 (54-66). From the use of clean fuels, the average attributable premature death rate (per 100 000 population) at the national level is 59 (53-64), the rural average being 68 (62-74) and the urban average 45 (41-50)., Interpretation: A shift from polluting to clean fuels can reduce the average PM 2·5 personal exposure by 53% and thereby lower the death rate. For all fuel types, the estimated average HAP-PM 2·5 personal exposure and indoor concentrations exceed the WHO's Interim Target-1 average annual threshold. Policy interventions are urgently needed to greatly increase the use of clean fuels and stove technologies by 2030 to achieve the goal of affordable clean energy access, as set by the UN in 2015, and address health inequities in urban-rural settings., Funding: Wellcome Trust, The Lancet Countdown, the Engineering and Physical Sciences Research Council, and the Natural Environment Research Council., Competing Interests: Declaration of interests We declare no competing interests., (Copyright © 2023 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Competing Interests: Declaration of interests We declare no competing interests.

Competing Interests: Declaration of interests CD was supported by the UK Natural Environment Research Council (NE/R010811/1) and the UK Natural Environment Research Council Independent Research Fellowship (NE/N01524X/1) and contributes to the Sustainable and Healthy Food Systems project supported by the Wellcome Trust (205200/Z/16/Z). MD was supported by the Wellcome Trust's Complex Urban Systems for Sustainability and Health (CUSSH) project (209387/Z/17/Z). YL was supported by the US National Aeronautics and Space Administration Applied Sciences Program (80NSSC21K0507). RL was supported by a Royal Society Dorothy Hodgkin Fellowship. MSo was supported by Horizon 2020 project EXHAUSTION (820655) and Academy of Finland HEATCOST (334798). SHG and JKWL were supported by Singapore's National Research Foundation, Singapore's Prime Minister's Office, under its Campus for Research Excellence and Technological Enterprise programme. IH was supported by the UK Research and Innovation (UKRI) Engineering and Physical Sciences Research Council Centre for Research in Energy Demand Solutions (EP/R035288/1) and UKRI APEx (NE/T001887/1). JM was supported by the German Ministry for Education and Research (01LA1826A and 03SFK5J0). ML was supported by the Sloan Foundation. All other authors declare no competing interests.

Competing Interests: Declaration of interests VK and OS are staff members of the WHO Regional Office for Europe. The authors alone are responsible for the views expressed in this publication and they do not necessarily represent the decisions or policies of the World Health Organization. The designations employed and the presentation of the material in this publication do not imply the expression of any opinion whatsoever on the part of WHO concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. Dotted and dashed lines on maps represent approximate border lines for which there may not yet be full agreement. All other authors declare no competing interests.

City clusters play an important role in air pollutant and greenhouse gas (GHG) emissions reduction in China, primarily due to their high fossil energy consumption levels. The "2 + 26" Cities, i.e., Beijing, Tianjin and 26 other perfectures in northern China, has experienced serious air pollution in recent years. We employ the Greenhouse Gas and Air Pollution Interactions and Synergies model adapted to the "2 + 26" Cities (GAINS-JJJ) to evaluate the impacts of structural adjustments in four major sectors, industry, energy, transport and land use, under the Three-Year Action Plan for Blue Skies (Three-Year Action Plan) on the emissions of both the major air pollutants and CO 2 in the "2 + 26" Cities. The results indicate that the Three-Year Action Plan applied in the "2 + 26" Cities reduces the total emissions of primary fine particulate matter with an aerodynamic diameter of ≤ 2.5 μm (PM 2.5 ), SO 2 , NO x , NH 3 and CO 2 by 17%, 25%, 21%, 3% and 1%, respectively, from 2017 to 2020. The emission reduction potentials vary widely across the 28 prefectures, which may be attributed to the differences in energy structure, industrial composition, and policy enforcement rate. Among the four sectors, adjustment of industrial structure attains the highest co-benefits of CO 2 reduction and air pollution control due to its high CO 2 reduction potential, while structural adjustments in energy and transport attain much lower co-benefits, despite their relatively high air pollutant emissions reductions, primarily resulting from an increase in the coal-electric load and associated carbon emissions caused by electric reform policies.., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

The rapidly rising generation of municipal solid waste jeopardizes the environment and contributes to climate heating. Based on the Shared Socioeconomic Pathways, we here develop a global systematic approach for evaluating the potentials to reduce emissions of greenhouse gases and air pollutants from the implementation of circular municipal waste management systems. We contrast two sets of global scenarios until 2050, namely baseline and mitigation scenarios, and show that mitigation strategies in the sustainability-oriented scenario yields earlier, and major, co-benefits compared to scenarios in which inequalities are reduced but that are focused solely on technical solutions. The sustainability-oriented scenario leaves 386 Tg CO 2eq /yr of GHG (CH 4 and CO 2 ) to be released while air pollutants from open burning can be eliminated, indicating that this source of ambient air pollution can be entirely eradicated before 2050., (© 2022. The Author(s).)

Competing Interests: Declaration of interests AM, PL, and IH were supported by the Wellcome Trust during this report. All other authors declare no conflicts of interests.

This paper describes a global research programme on the complex systemic connections between urban development and health. Through transdisciplinary methods the Complex Urban Systems for Sustainability and Health (CUSSH) project will develop critical evidence on how to achieve the far-reaching transformation of cities needed to address vital environmental imperatives for planetary health in the 21st Century. CUSSH's core components include: (i) a review of evidence on the effects of climate actions (both mitigation and adaptation) and factors influencing their implementation in urban settings; (ii) the development and application of methods for tracking the progress of cities towards sustainability and health goals; (iii) the development and application of models to assess the impact on population health, health inequalities, socio-economic development and environmental parameters of urban development strategies, in order to support policy decisions; (iv) iterative in-depth engagements with stakeholders in partner cities in low-, middle- and high-income settings, using systems-based participatory methods, to test and support the implementation of the transformative changes needed to meet local and global health and sustainability objectives; (v) a programme of public engagement and capacity building. Through these steps, the programme will provide transferable evidence on how to accelerate actions essential to achieving population-level health and global climate goals through, amongst others, changing cities' energy provision, transport infrastructure, green infrastructure, air quality, waste management and housing., Competing Interests: No competing interests were disclosed., (Copyright: © 2021 Davies M et al.)