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How to improve healthcare improvement—an essay by Mary Dixon-Woods

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• Mary Dixon-Woods , director
• THIS Institute, Cambridge, UK
• director{at}thisinstitute.cam.ac.uk

As improvement practice and research begin to come of age, Mary Dixon-Woods considers the key areas that need attention if we are to reap their benefits

In the NHS, as in health systems worldwide, patients are exposed to risks of avoidable harm 1 and unwarranted variations in quality. 2 3 4 But too often, problems in the quality and safety of healthcare are merely described, even “admired,” 5 rather than fixed; the effort invested in collecting information (which is essential) is not matched by effort in making improvement. The National Confidential Enquiry into Patient Outcome and Death, for example, has raised many of the same concerns in report after report. 6 Catastrophic degradations of organisations and units have recurred throughout the history of the NHS, with depressingly similar features each time. 7 8 9

More resources are clearly necessary to tackle many of these problems. There is no dispute about the preconditions for high quality, safe care: funding, staff, training, buildings, equipment, and other infrastructure. But quality health services depend not just on structures but on processes. 10 Optimising the use of available resources requires continuous improvement of healthcare processes and systems. 5

The NHS has seen many attempts to stimulate organisations to improve using incentive schemes, ranging from pay for performance (the Quality and Outcomes Framework in primary care, for example) to public reporting (such as annual quality accounts). They have had mixed results, and many have had unintended consequences. 11 12 Wanting to improve is not the same as knowing how to do it.

In response, attention has increasingly turned to a set of approaches known as quality improvement (QI). Though a definition of exactly what counts as a QI approach has escaped consensus, QI is often identified with a set of techniques adapted from industrial settings. They include the US Institute for Healthcare Improvement’s Model for Improvement, which, among other things, combines measurement with tests of small change (plan-do-study-act cycles). 8 Other popular approaches include Lean and Six Sigma. QI can also involve specific interventions intended to improve processes and systems, ranging from checklists and “care bundles” of interventions (a set of evidence based practices intended to be done consistently) through to medicines reconciliation and clinical pathways.

QI has been advocated in healthcare for over 30 years 13 ; policies emphasise the need for QI and QI practice is mandated for many healthcare professionals (including junior doctors). Yet the question, “Does quality improvement actually improve quality?” remains surprisingly difficult to answer. 14 The evidence for the benefits of QI is mixed 14 and generally of poor quality. It is important to resolve this unsatisfactory situation. That will require doing more to bring together the practice and the study of improvement, using research to improve improvement, and thinking beyond effectiveness when considering the study and practice of improvement.

Uniting practice and study

The practice and study of improvement need closer integration. Though QI programmes and interventions may be just as consequential for patient wellbeing as drugs, devices, and other biomedical interventions, research about improvement has often been seen as unnecessary or discretionary, 15 16 particularly by some of its more ardent advocates. This is partly because the challenges faced are urgent, and the solutions seem obvious, so just getting on with it seems the right thing to do.

But, as in many other areas of human activity, QI is pervaded by optimism bias. It is particularly affected by the “lovely baby” syndrome, which happens when formal evaluation is eschewed because something looks so good that it is assumed it must work. Five systematic reviews (published 2010-16) reporting on evaluations of Lean and Six Sigma did not identify a single randomised controlled trial. 17 18 19 20 21 A systematic review of redesigning care processes identified no randomised trials. 22 A systematic review of the application of plan-do-study-act in healthcare identified no randomised trials. 23 A systematic review of several QI methods in surgery identified just one randomised trial. 56

The sobering reality is that some well intentioned, initially plausible improvement efforts fail when subjected to more rigorous evaluation. 24 For instance, a controlled study of a large, well resourced programme that supported a group of NHS hospitals to implement the IHI’s Model for Improvement found no differences in the rate of improvement between participating and control organisations. 25 26 Specific interventions may, similarly, not survive the rigours of systematic testing. An example is a programme to reduce hospital admissions from nursing homes that showed promise in a small study in the US, 27 but a later randomised implementation trial found no effect on admissions or emergency department attendances. 28

Some interventions are probably just not worth the effort and opportunity cost: having nurses wear “do not disturb” tabards during drug rounds, is one example. 29 And some QI efforts, perversely, may cause harm—as happened when a multicomponent intervention was found to be associated with an increase rather than a decrease in surgical site infections. 30

Producing sound evidence for the effectiveness of improvement interventions and programmes is likely to require a multipronged approach. More large scale trials and other rigorous studies, with embedded qualitative inquiry, should be a priority for research funders.

Not every study of improvement needs to be a randomised trial. One valuable but underused strategy involves wrapping evaluation around initiatives that are happening anyway, especially when it is possible to take advantage of natural experiments or design roll-outs. 31 Evaluation of the reorganisation of stroke care in London and Manchester 32 and the study of the Matching Michigan programme to reduce central line infections are good examples. 33 34

It would be impossible to externally evaluate every QI project. Critically important therefore will be increasing the rigour with which QI efforts evaluate themselves, as shown by a recent study of an attempt to improve care of frail older people using a “hospital at home” approach in southwest England. 35 This ingeniously designed study found no effect on outcomes and also showed that context matters.

Despite the potential value of high quality evaluation, QI reports are often weak, 18 with, for example, interventions so poorly reported that reproducibility is frustrated. 36 Recent reporting guidelines may help, 37 but some problems are not straightforward to resolve. In particular, current structures for governance and publishing research are not always well suited to QI, including situations where researchers study programmes they have not themselves initiated. Systematic learning from QI needs to improve, which may require fresh thinking about how best to align the goals of practice and study, and to reconcile the needs of different stakeholders. 38

Using research to improve improvement

Research can help to support the practice of improvement in many ways other than evaluation of its effectiveness. One important role lies in creating assets that can be used to improve practice, such as ways to visualise data, analytical methods, and validated measures that assess the aspects of care that most matter to patients and staff. This kind of work could, for example, help to reduce the current vast number of quality measures—there are more than 1200 indicators of structure and process in perioperative care alone. 39

The study of improvement can also identify how improvement practice can get better. For instance, it has become clear that fidelity to the basic principles of improvement methods is a major problem: plan-do-study-act cycles are crucial to many improvement approaches, yet only 20% of the projects that report using the technique have done so properly. 23 Research has also identified problems in measurement—teams trying to do improvement may struggle with definitions, data collection, and interpretation 40 —indicating that this too requires more investment.

Improvement research is particularly important to help cumulate, synthesise, and scale learning so that practice can move forward without reinventing solutions that already exist or reintroducing things that do not work. Such theorising can be highly practical, 41 helping to clarify the mechanisms through which interventions are likely to work, supporting the optimisation of those interventions, and identifying their most appropriate targets. 42

Research can systematise learning from “positive deviance,” approaches that examine individuals, teams, or organisations that show exceptionally good performance. 43 Positive deviance can be used to identify successful designs for clinical processes that other organisations can apply. 44

Crucially, positive deviance can also help to characterise the features of high performing contexts and ensure that the right lessons are learnt. For example, a distinguishing feature of many high performing organisations, including many currently rated as outstanding by the Care Quality Commission, is that they use structured methods of continuous quality improvement. But studies of high performing settings, such as the Southmead maternity unit in Bristol, indicate that although continuous improvement is key to their success, a specific branded improvement method is not necessary. 45 This and other work shows that not all improvement needs to involve a well defined QI intervention, and not everything requires a discrete project with formal plan-do-study-act cycles.

More broadly, research has shown that QI is just one contributor to improving quality and safety. Organisations in many industries display similar variations to healthcare organisations, including large and persistent differences in performance and productivity between seemingly similar enterprises. 46 Important work, some of it experimental, is beginning to show that it is the quality of their management practices that distinguishes them. 47 These practices include continuous quality improvement as well as skills training, human resources, and operational management, for example. QI without the right contextual support is likely to have limited impact.

Beyond effectiveness

Important as they are, evaluations of the approaches and interventions in individual improvement programmes cannot answer every pertinent question about improvement. 48 Other key questions concern the values and assumptions intrinsic to QI.

Consider the “product dominant” logic in many healthcare improvement efforts, which assumes that one party makes a product and conveys it to a consumer. 49 Paul Batalden, one of the early pioneers of QI in healthcare, proposes that we need instead a “service dominant” logic, which assumes that health is co-produced with patients. 49

More broadly, we must interrogate how problems of quality and safety are identified, defined, and selected for attention by whom, through which power structures, and with what consequences. Why, for instance, is so much attention given to individual professional behaviour when systems are likely to be a more productive focus? 50 Why have quality and safety in mental illness and learning disability received less attention in practice, policy, and research 51 despite high morbidity and mortality and evidence of both serious harm and failures of organisational learning? The concern extends to why the topic of social inequities in healthcare improvement has remained so muted 52 and to the choice of subjects for study. Why is it, for example, that interventions like education and training, which have important roles in quality and safety and are undertaken at vast scale, are often treated as undeserving of evaluation or research?

How QI is organised institutionally also demands attention. It is often conducted as a highly local, almost artisan activity, with each organisation painstakingly working out its own solution for each problem. Much improvement work is conducted by professionals in training, often in the form of small, time limited projects conducted for accreditation. But working in this isolated way means a lack of critical mass to support the right kinds of expertise, such as the technical skill in human factors or ergonomics necessary to engineer a process or devise a safety solution. Having hundreds of organisations all trying to do their own thing also means much waste, and the absence of harmonisation across basic processes introduces inefficiencies and risks. 14

A better approach to the interorganisational nature of health service provision requires solving the “problem of many hands.” 53 We need ways to agree which kinds of sector-wide challenges need standardisation and interoperability; which solutions can be left to local customisation at implementation; and which should be developed entirely locally. 14 Better development of solutions and interventions is likely to require more use of prototyping, modelling and simulation, and testing in different scenarios and under different conditions, 14 ideally through coordinated, large scale efforts that incorporate high quality evaluation.

Finally, an approach that goes beyond effectiveness can also help in recognising the essential role of the professions in healthcare improvement. The past half century has seen a dramatic redefining of the role and status of the healthcare professions in health systems 54 : unprecedented external accountability, oversight, and surveillance are now the norm. But policy makers would do well to recognise how much more can be achieved through professional coalitions of the willing than through too many imposed, compliance focused diktats. Research is now showing how the professions can be hugely important institutional forces for good. 54 55 In particular, the professions have a unique and invaluable role in working as advocates for improvement, creating alliances with patients, providing training and education, contributing expertise and wisdom, coordinating improvement efforts, and giving political voice for problems that need to be solved at system level (such as, for example, equipment design).

Improvement efforts are critical to securing the future of the NHS. But they need an evidence base. Without sound evaluation, patients may be deprived of benefit, resources and energy may be wasted on ineffective QI interventions or on interventions that distribute risks unfairly, and organisations are left unable to make good decisions about trade-offs given their many competing priorities. The study of improvement has an important role in developing an evidence-base and in exploring questions beyond effectiveness alone, and in particular showing the need to establish improvement as a collective endeavour that can benefit from professional leadership.

Mary Dixon-Woods is the Health Foundation professor of healthcare improvement studies and director of The Healthcare Improvement Studies (THIS) Institute at the University of Cambridge, funded by the Health Foundation. Co-editor-in-chief of BMJ Quality and Safety , she is an honorary fellow of the Royal College of General Practitioners and the Royal College of Physicians. This article is based largely on the Harveian oration she gave at the RCP on 18 October 2018, in the year of the college’s 500th anniversary. The oration is available here: http://www.clinmed.rcpjournal.org/content/19/1/47 and the video version here: https://www.rcplondon.ac.uk/events/harveian-oration-and-dinner-2018

This article is one of a series commissioned by The BMJ based on ideas generated by a joint editorial group with members from the Health Foundation and The BMJ , including a patient/carer. The BMJ retained full editorial control over external peer review, editing, and publication. Open access fees and The BMJ ’s quality improvement editor post are funded by the Health Foundation.

Competing interests: I have read and understood BMJ policy on declaration of interests and a statement is available here: https://www.bmj.com/about-bmj/advisory-panels/editorial-advisory-board/mary-dixonwoods

Provenance and peer review: Commissioned; not externally peer reviewed.

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ .

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Essay on Self Improvement

Students are often asked to write an essay on Self Improvement in their schools and colleges. And if you’re also looking for the same, we have created 100-word, 250-word, and 500-word essays on the topic.

Let’s take a look…

100 Words Essay on Self Improvement

What is self improvement.

Self-improvement is when you make yourself better. You can do this in many different ways. It could be learning a new thing, changing a bad habit, or becoming kinder. It’s about making positive changes to yourself.

Why is Self Improvement Important?

Self-improvement is important because it helps us grow. It helps us become better people. When we improve ourselves, we feel happier and more confident. We can do more things and achieve our goals.

How to Start Self Improvement?

To start self-improvement, you need to think about what you want to change. You might want to learn a new skill or stop a bad habit. Then, you make a plan and start working on it.

Tips for Self Improvement

Here are some tips for self-improvement: set clear goals, be patient, and keep going even when it’s hard. Also, remember to reward yourself when you achieve your goals. This will keep you motivated.

In conclusion, self-improvement is a journey. It takes time and effort. But it’s worth it because you become a better person. So, start your self-improvement journey today!

Also check:

• Speech on Self Improvement

250 Words Essay on Self Improvement

Self Improvement is the act of making yourself better. It’s about learning new things and getting rid of bad habits. It’s like planting a seed and taking care of it until it grows into a big, strong tree.

Self Improvement is important because it helps us grow and become better people. It’s like climbing a ladder. Each step we take brings us closer to the top. We learn new things, become better at what we do, and feel happier. It’s a journey that never ends.

Starting Self Improvement is easy. First, think about what you want to improve. It could be anything. Maybe you want to read more books, or learn a new skill, or be kinder to people. Then, make a plan. Write down what you want to do and how you will do it. And then, start doing it. It’s as simple as that.

Challenges in Self Improvement

Self Improvement can be hard. Sometimes, we might feel like giving up. But remember, every step counts. Even small changes can make a big difference. And if you keep going, you will get better. It’s like a journey. There might be bumps along the way, but in the end, you will reach your destination.

Self Improvement is a journey of learning and growing. It’s about becoming the best version of ourselves. It might be hard, but it’s worth it. So, let’s start our journey of Self Improvement today. Let’s plant our seeds and watch them grow into strong, beautiful trees.

500 Words Essay on Self Improvement

Understanding self-improvement.

Self-improvement is the process of making yourself a better person. It is about learning new things, developing new skills, and becoming a better version of yourself. It can include many things like learning a new language, improving your physical health, or becoming a better listener.

The Importance of Self-Improvement

Self-improvement is important for many reasons. It can help you achieve your goals, make you feel good about yourself, and improve your life in general. For example, if you want to become a better student, you might focus on improving your study habits. This could include things like setting aside more time for studying, learning new study techniques, or finding a study group to join.

Ways to Improve Yourself

There are many ways you can work on self-improvement. One way is to set goals for yourself. These goals should be things that you want to achieve or improve about yourself. They could be big goals like getting into a good college, or smaller goals like reading a new book every month.

Another way to improve yourself is to learn new skills. This could be anything from learning how to cook a new recipe, to learning how to play a musical instrument, to learning how to code. Learning new skills can be fun and rewarding, and it can also help you become a more well-rounded person.

Challenges in Self-Improvement

While self-improvement can be rewarding, it can also be challenging. Sometimes, it can be hard to stick to your goals, especially if they are big and take a long time to achieve. It can also be hard to find the time to work on self-improvement, especially if you have a busy schedule.

Despite these challenges, it’s important to remember that self-improvement is a journey, not a destination. It’s about making progress, not about being perfect. Even small steps towards your goals can make a big difference.

The Role of Patience in Self-Improvement

Patience plays a key role in self-improvement. It’s important to remember that change takes time. You won’t become a better person overnight. It takes time, effort, and patience. But with persistence, you can make progress and see improvements in yourself.

In conclusion, self-improvement is a lifelong journey. It’s about setting goals, learning new skills, and overcoming challenges. It’s about becoming a better version of yourself, one step at a time. So, keep moving forward, keep learning, and keep improving. The journey of self-improvement is a rewarding one, and it’s a journey that’s worth taking.

That’s it! I hope the essay helped you.

If you’re looking for more, here are essays on other interesting topics:

• Essay on Cosmetic Surgery
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• Essay on Corruption Is The Root Cause Of Poverty

Apart from these, you can look at all the essays by clicking here .

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Instruction fine-tuning

To fully unlock the potential of our pretrained models in chat use cases, we innovated on our approach to instruction-tuning as well. Our approach to post-training is a combination of supervised fine-tuning (SFT), rejection sampling, proximal policy optimization (PPO), and direct preference optimization (DPO). The quality of the prompts that are used in SFT and the preference rankings that are used in PPO and DPO has an outsized influence on the performance of aligned models. Some of our biggest improvements in model quality came from carefully curating this data and performing multiple rounds of quality assurance on annotations provided by human annotators.

Learning from preference rankings via PPO and DPO also greatly improved the performance of Llama 3 on reasoning and coding tasks. We found that if you ask a model a reasoning question that it struggles to answer, the model will sometimes produce the right reasoning trace: The model knows how to produce the right answer, but it does not know how to select it. Training on preference rankings enables the model to learn how to select it.

Building with Llama 3

Our vision is to enable developers to customize Llama 3 to support relevant use cases and to make it easier to adopt best practices and improve the open ecosystem. With this release, we’re providing new trust and safety tools including updated components with both Llama Guard 2 and Cybersec Eval 2, and the introduction of Code Shield—an inference time guardrail for filtering insecure code produced by LLMs.

We’ve also co-developed Llama 3 with torchtune , the new PyTorch-native library for easily authoring, fine-tuning, and experimenting with LLMs. torchtune provides memory efficient and hackable training recipes written entirely in PyTorch. The library is integrated with popular platforms such as Hugging Face, Weights & Biases, and EleutherAI and even supports Executorch for enabling efficient inference to be run on a wide variety of mobile and edge devices. For everything from prompt engineering to using Llama 3 with LangChain we have a comprehensive getting started guide and takes you from downloading Llama 3 all the way to deployment at scale within your generative AI application.

A system-level approach to responsibility

We have designed Llama 3 models to be maximally helpful while ensuring an industry leading approach to responsibly deploying them. To achieve this, we have adopted a new, system-level approach to the responsible development and deployment of Llama. We envision Llama models as part of a broader system that puts the developer in the driver’s seat. Llama models will serve as a foundational piece of a system that developers design with their unique end goals in mind.

Instruction fine-tuning also plays a major role in ensuring the safety of our models. Our instruction-fine-tuned models have been red-teamed (tested) for safety through internal and external efforts. ​​Our red teaming approach leverages human experts and automation methods to generate adversarial prompts that try to elicit problematic responses. For instance, we apply comprehensive testing to assess risks of misuse related to Chemical, Biological, Cyber Security, and other risk areas. All of these efforts are iterative and used to inform safety fine-tuning of the models being released. You can read more about our efforts in the model card .

Llama Guard models are meant to be a foundation for prompt and response safety and can easily be fine-tuned to create a new taxonomy depending on application needs. As a starting point, the new Llama Guard 2 uses the recently announced MLCommons taxonomy, in an effort to support the emergence of industry standards in this important area. Additionally, CyberSecEval 2 expands on its predecessor by adding measures of an LLM’s propensity to allow for abuse of its code interpreter, offensive cybersecurity capabilities, and susceptibility to prompt injection attacks (learn more in our technical paper ). Finally, we’re introducing Code Shield which adds support for inference-time filtering of insecure code produced by LLMs. This offers mitigation of risks around insecure code suggestions, code interpreter abuse prevention, and secure command execution.

With the speed at which the generative AI space is moving, we believe an open approach is an important way to bring the ecosystem together and mitigate these potential harms. As part of that, we’re updating our Responsible Use Guide (RUG) that provides a comprehensive guide to responsible development with LLMs. As we outlined in the RUG, we recommend that all inputs and outputs be checked and filtered in accordance with content guidelines appropriate to the application. Additionally, many cloud service providers offer content moderation APIs and other tools for responsible deployment, and we encourage developers to also consider using these options.

Deploying Llama 3 at scale

Llama 3 will soon be available on all major platforms including cloud providers, model API providers, and much more. Llama 3 will be everywhere .

Our benchmarks show the tokenizer offers improved token efficiency, yielding up to 15% fewer tokens compared to Llama 2. Also, Group Query Attention (GQA) now has been added to Llama 3 8B as well. As a result, we observed that despite the model having 1B more parameters compared to Llama 2 7B, the improved tokenizer efficiency and GQA contribute to maintaining the inference efficiency on par with Llama 2 7B.

For examples of how to leverage all of these capabilities, check out Llama Recipes which contains all of our open source code that can be leveraged for everything from fine-tuning to deployment to model evaluation.

What’s next for Llama 3?

The Llama 3 8B and 70B models mark the beginning of what we plan to release for Llama 3. And there’s a lot more to come.

Our largest models are over 400B parameters and, while these models are still training, our team is excited about how they’re trending. Over the coming months, we’ll release multiple models with new capabilities including multimodality, the ability to converse in multiple languages, a much longer context window, and stronger overall capabilities. We will also publish a detailed research paper once we are done training Llama 3.

To give you a sneak preview for where these models are today as they continue training, we thought we could share some snapshots of how our largest LLM model is trending. Please note that this data is based on an early checkpoint of Llama 3 that is still training and these capabilities are not supported as part of the models released today.

We’re committed to the continued growth and development of an open AI ecosystem for releasing our models responsibly. We have long believed that openness leads to better, safer products, faster innovation, and a healthier overall market. This is good for Meta, and it is good for society. We’re taking a community-first approach with Llama 3, and starting today, these models are available on the leading cloud, hosting, and hardware platforms with many more to come.

Try Meta Llama 3 today

We’ve integrated our latest models into Meta AI, which we believe is the world’s leading AI assistant. It’s now built with Llama 3 technology and it’s available in more countries across our apps.

You can use Meta AI on Facebook, Instagram, WhatsApp, Messenger, and the web to get things done, learn, create, and connect with the things that matter to you. You can read more about the Meta AI experience here .

Visit the Llama 3 website to download the models and reference the Getting Started Guide for the latest list of all available platforms.

You’ll also soon be able to test multimodal Meta AI on our Ray-Ban Meta smart glasses.

As always, we look forward to seeing all the amazing products and experiences you will build with Meta Llama 3.

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How did once smelly Jacksonville area fare in annual State of the Air pollution report

For the third year in a row , the Jacksonville metropolitan area was named one of the cleanest in the nation, with an "A" grade for ozone pollution, according to an American Lung Association report.

The metropolitan area, which extends to St. Marys, Ga., and Palatka, had a "B" grade for short-term spikes in particle pollution. Still, that ranking — based on the area’s average number of unhealthy days — was better than last year's level, according to the report.

The State of the Air report ranks communities across the nation for "exposure to unhealthy levels of ground-level ozone air pollution, annual particle pollution and short-term spikes in particle pollution over a three-year period," according to the association This year’s 25th annual report includes 2020 through 2022 air quality data and the latest U.S. Environmental Protection Agency particle pollution standard.

Ozone and particle pollution can cause asthma attacks, heart attacks, strokes, preterm births and impaired cognitive functioning later in life, as well as premature death. Particle pollution can also cause lung cancer, according to the association.

"We have seen incredible improvement in the nation’s air quality," said Ashley Lyerly, the association's senior director of advocacy. "Unfortunately, more than 131 million people still live in places with unhealthy levels of air pollution. Climate change is making air pollution more likely to form and more difficult to clean up."

What is ozone and how have the Jacksonville area's levels improved?

Ozone is a gas molecule. The ozone layer in the earth's upper atmosphere "shields us from much of the sun's ultraviolet radiation. However, ozone air pollution at ground level … aggressively attacks lung tissue by reacting chemically with it," according to the association.

Ground-level ozone develops from pollution emitted from cars, power plants, industrial boilers, refineries and chemical plants and can also stem from paints, cleaners, solvents and motorized lawn equipment, according to the association.

Nationwide, ozone is the "air pollutant affecting the largest number of people," the association said.

The Jacksonville area received an "A" ozone grade because it had no unhealthy days. Only 24 years ago, in 2000, the city received an "F," with 15 days of bad ozone levels, tying for third-worst in Florida.

How did Jacksonville fare in short-term and long-term particle pollution rankings?

Particle pollution is a " mixture of solid particles and liquid droplets found in the air. Some particles, such as dust, dirt, soot or smoke, are large or dark enough to be seen with the naked eye," according to the EPA. "Others are so small they can only be detected using an electron microscope."

Such pollution can come from a variety of sources, including construction sites, unpaved roads, fields, smokestacks or fires. Most particles "form in the atmosphere as a result of complex reactions of chemicals such as sulfur dioxide and nitrogen oxides, which are pollutants emitted from power plants, industries and automobiles," according to the EPA.

From 2023: Jacksonville air quality slips as smoke from Canadian fires spreads; no advisories needed

The Jacksonville metro area ranked 124th worst in the nation for short-term particle pollution, with 0.3 unhealthy days per year, a "B” grade. Last year, the area was 118th worst. As for the year-round average of long-term particle pollution, the area received a passing grade and was 84th worst in the nation, compared to last year's 89th worst ranking.

Jacksonville has overcome its past reputation as a smelly, polluted city?

Yes. The American Lung Association's Lyerly has said Jacksonville had a "historical" reputation as one of the "most-polluted" cities in the country. But in recent years, the city "is one of the cleanest communities for ozone," she said, and has improved its particle pollution.

"In February 1949, soot from Jacksonville smokestacks was blamed for  women’s nylon hosiery dissolving  on their legs, a spectacle that made national news and  fueled some of the first research into the city’s air pollution ," according to a 2022 Times-Union report .

Air pollution complaints stirred objections to smells from paper mills and other factories, according to the Times-Union report.

What U.S. populations are the most affected by air pollution?

"Communities of color are disproportionately exposed to unhealthy air and are also more likely to be living with one or more chronic conditions that make them more vulnerable to air pollution," such as asthma , diabetes and heart disease, according to the association.

"A person of color in the U.S. is more than twice as likely as a white individual to live in a community with a failing grade on all three pollution measures," the association said.

What actions are needed to further improve U.S. air pollution?

The association attributes the improvement in Jacksonville and across the country to the  Clean Air Act  of 1970, which regulated air emissions from stationary and mobile sources and authorized the EPA to establish national air quality standards.

But climate change can worsen air pollution by creating " conditions , including heat and stagnant air, which … make ozone more likely to form. Even with current measures to reduce ozone, evidence warns that climate change will likely increase ozone levels in large parts of the U.S.," according to the association.

Newly enacted EPA air pollution rules "will help clean up particle pollution and address climate change," according to the association. But the nonprofit is also seeking what it called "long overdue stronger national limits on ozone pollution."

"Stronger limits would help people protect themselves and drive cleanup of polluting sources across the country," according to the association.

The association also advocates moving from fossil-fuel-fired power plants to non-combustion, renewable energy; reducing pollution, including methane emissions, at oil and gas drilling operations; improving energy efficiency; and using zero-emission transportation.

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