Health and Economic Benefits of Accelerating the PM₁₀ Interim Targets in Brazil’s New Air Quality Resolution: A Case Study in Southern Brazil

Abstract

Air pollution, particularly from coarse particulate matter (PM₁₀), is a major public health concern, significantly contributing to respiratory and cardiovascular diseases, especially among vulnerable populations. In 2024, Brazil introduced a new air quality resolution (CONAMA Resolution No. 506/2024), aligning its ultimate goal with the World Health Organization’s 2021 guidelines while establishing specific timelines for the interim targets. However, these interim targets, set for 2025, 2033, and 2044, along with the absence of a deadline for the final target of 15 µg/m³, raise concerns about their adequacy in addressing the urgent health impacts of air pollution. This study evaluates the economic and public health benefits of accelerating these targets in the city of Rio Grande, a region characterized by an industrial and port-driven economy and an aging population particularly vulnerable to air pollution. Using health impact assessments, economic cost analyses, and mortality estimates, we modeled three scenarios with PM₁₀ concentration limits of 30 µg/m³, 20 µg/m³, and 15 µg/m³, corresponding to the resolution’s 2033 and 2044 goals and the undated final target. Our findings indicate that achieving the 15 µg/m³ target by 2025 could prevent 2568 respiratory hospitalizations, 1551 cardiac hospitalizations, and 1128 air pollution-related deaths in Rio Grande, resulting in approximately BRL 7.3 million in healthcare savings. When extrapolated to cities with similar pollution profiles across Brazil, these results suggest substantial potential for reducing the health burdens and economic costs nationwide. This study underscores the urgent need to establish more ambitious timelines in Brazil’s air quality policies to maximize public health benefits and mitigate the economic impacts of air pollution.

1. Introduction

Air pollution is the leading environmental risk factor for human health [1], significantly contributing to increased morbidity and mortality, particularly in urban areas. In 2021, air pollution accounted for 8.1 million deaths globally, making it the second most common cause of premature mortality after hypertension [2]. Among air pollutants, coarse particulate matter with an aerodynamic diameter of less than 10 μm (PM₁₀) has been strongly associated with the onset and exacerbation of respiratory and cardiovascular diseases [3,4,5,6,7,8]. These particles, primarily originating from fossil fuel combustion, industrial emissions, urban dust, and agricultural activities, disproportionately affect vulnerable populations such as the elderly and individuals with pre-existing comorbidities [9,10,11,12,13,14,15]. Longitudinal studies have linked elevated PM₁₀ levels to the development of new atherosclerotic lesions and increased risks of myocardial infarction, particularly in individuals with cardiovascular disease or a high body mass index [16,17]. Moreover, recent research demonstrates that reducing PM₁₀ levels correlates with decreased public healthcare costs and fewer hospitalizations for respiratory and cardiovascular diseases [18,19,20,21,22].

Since the establishment of Brazil’s first air quality resolution, CONAMA Resolution No. 03/1990 [23], the country has faced persistent challenges, including vague guidelines, outdated standards compared to the international recommendations, and the absence of concrete deadlines for achieving its interim targets [24]. Even where Brazil updated its air pollutant limit values to align with the World Health Organization (WHO)’s 2005 guidelines through CONAMA Resolution No. 491/2018 [25], the lack of a progressive strategy and well-defined timelines limited its effectiveness [24]. Furthermore, this resolution quickly became outdated following the release of the revised WHO air quality guideline values in 2021 [26].

In a significant policy shift and in response to the growing health impacts of air pollution, Brazil undertook a comprehensive revision of its air quality policy framework. This effort culminated in the enactment of CONAMA Resolution No. 506 on 5 July 2024 [27], which introduced specific deadlines for the interim air quality targets and aligned its ultimate goal with the 2021 WHO guidelines [26]. The resolution established phased targets for 2025, 2033, and 2044, marking a considerable improvement in strategic planning. Additionally, it incorporated innovative measures such as regulating the Air Quality Index and mandating continuous monitoring by state entities, thereby enhancing the transparency and accessibility of air quality data. Despite these advancements, as highlighted by Tavella et al. [24], the extended timelines for achieving these targets and the absence of a defined deadline for the final goal reflect a cautious approach that may fail to address the urgency of reducing air pollution’s health impacts.

The link between air quality and human health is well documented [3,9,28,29,30,31,32,33,34,35,36,37,38], with PM₁₀ pollution emerging as a major contributor to chronic diseases [4,6,7,8,39,40,41,42,43,44,45]. Persistently high PM₁₀ levels not only exacerbate respiratory and cardiovascular conditions but also impose significant economic and social costs. Implementing public policies that prioritize reductions in PM₁₀ is not merely an environmental health initiative but a direct investment in improving quality of life and reducing pressures on the healthcare system. Accelerating efforts to meet the global standards is essential to ensuring cleaner air and healthier communities.

The city of Rio Grande, located in Rio Grande do Sul State, southern Brazil, exemplifies the complexities of addressing air pollution. While its geographic characteristics facilitate pollutant dispersion during certain seasons, its economic characteristics contribute to high particulate matter concentrations, driven by industrial activities such as refineries, food processing, metallurgy, and intense port operations [46]. Notably, this city’s lack of a clear distinction between industrial and urban zones, with industrial areas often being adjacent to or overlapping with residential neighborhoods, intensifies human exposure to air pollution. For reasons shaped by these and other factors, the city is the subject of ongoing investigations into air pollution and its impacts, further highlighting its relevance for understanding and addressing these challenges [47,48,49,50,51]. Furthermore, the city’s aging population exacerbates the health impacts of air pollution [52]. Additionally, a recent study conducted in the metropolitan region of Rio Grande do Sul, spanning two decades of data (2002–2021), demonstrated the significant health impacts of air pollution on the local population, underscoring the urgency of mitigating air quality issues in this state [53].

In light of the above, our study sought to analyze the economic and public health benefits of accelerating the PM₁₀ reduction targets in Rio Grande, focusing on reducing the healthcare costs associated with hospitalizations for respiratory and cardiovascular diseases and estimating the decrease in non-external mortality attributable to air pollution. The central hypothesis is that advancing these targets would generate significant healthcare savings while reducing mortality. By analyzing the hospitalization data and atmospheric PM₁₀ concentrations in Rio Grande, this study seeks to provide robust evidence supporting the need to expedite the targets outlined in CONAMA’s resolution [27]. These findings contribute to the international discourse on the intersection of environmental regulation and public health, emphasizing the economic and health benefits of more ambitious air quality policies.

2. Materials and Methods

2.1. The Study Area

The city of Rio Grande (32°01′40″ S; 52°05′40″ W), located in the state of Rio Grande do Sul, Brazil (Figure 1), spans an area of approximately 2683 km² and has a population of around 191 thousand inhabitants [54]. The local economy relies heavily on a large industrial complex, including industries such as fertilizers, petroleum, food processing, fishery, and metallurgy, as well as intense port activities. The port of Rio Grande is the fifth busiest in Brazil [55]. In this city, industrial and urban zones are closely intertwined, with residential areas often being situated near industrial activities, which increases the potential for human exposure to air pollutants. This region features a subtropical climate characterized by moderate humidity, cold winters, mild summers, and abundant rainfall distributed throughout the year [56].

2.2. The Sampling Procedure and Monitoring Period

PM₁₀ concentration data were collected by the company Catavento Meteorologia at three sampling sites located within the influence area of the TECON Rio Grande container terminal, as illustrated in Figure 1. The georeferenced locations were as follows: Site 1 (32°7′49.27″ S, 52°6′11.72″ W), Site 2 (32°7′34.19″ S, 52°6′39.57″ W), and Site 3 (32°7′21.08″ S, 52°6′12.82″ W). The primary sources of pollutants in this area included industrial emissions and emissions from truck and heavy vehicle exhausts during container-loading and -unloading activities. These sites are adjacent to urban neighborhoods, where residential areas are directly influenced by industrial and urban emissions, capturing pollution from a diverse range of sources. This proximity to both industrial and residential zones ensures the representativeness of the collected data, making these sampling locations well suited to assessing the combined health impacts of urban and industrial air pollution in Rio Grande. Furthermore, the sampling sites were selected based on the pollutant source types, the prevailing wind direction in the region, and the availability of infrastructure for air quality monitoring stations.

PM₁₀ monitoring was conducted following the methodology outlined in the NBR 13.412 standard of the Brazilian Association of Technical Standards (ABNT), using active air sampling procedures and equipment [57]. The method employed high-volume samplers equipped with inertial separators calibrated to retain particles with an aerodynamic diameter ≤10 µm. Each sampling point was operated for 24 consecutive hours, with the air flows controlled between 1.02 and 1.24 m³/min, ensuring representative collection even under dynamic meteorological conditions. The particulate matter was deposited onto pre-conditioned fiberglass filters stabilized in the laboratory for gravimetric control. After collection, the filters were subjected to precise analytical weighing before and after exposure to determine the mass of the retained particles. The concentration was calculated as the ratio between the mass collected and the total volume of air sampled. The evaluation period spanned from January 2017 to November 2019. Data were provided by Catavento Meteorologia in the form of air quality reports, with each containing the 24 h average PM₁₀ concentrations for the three monitoring sites.

2.3. Health Data Acquisition

Health-related data, including total population, the number of hospitalizations due to cardiac diseases (I00-I52) across all age groups, and respiratory diseases (J00-J99) segmented by age groups (all ages, 15–64 years, and 65 years or older), as well as the average cost of hospitalizations, were obtained from the database of the Unified Health System’s Informatics Department (DATASUS) [58].

2.4. The Health Impact Assessment

The health impact assessment (HIA) was performed using the “tool short term” spreadsheet (rev. Sept 2013) developed by the APHEKOM project (Improving Knowledge and Communication for Decision Making on Air Pollution and Health in Europe). This tool provides insights and methodologies to assist policymakers in implementing more effective air quality strategies [59]. Despite being widely used in Europe, this methodology has also been applied in Brazil, including studies conducted in cities within the state of Rio Grande do Sul [52] and major Brazilian capital cities [53,60,61,62,63], demonstrating its applicability in the Brazilian context. In this study, the spreadsheet was customized using local data specific to Rio Grande, ensuring the methodology aligned with the city’s individual characteristics.

Three scenarios were simulated to assess their health benefits, corresponding to annual PM₁₀ concentrations set at the new limits proposed by CONAMA Resolution No. 506/2024: 30 µg/m³, 20 µg/m³, and 15 µg/m³. The health benefits associated with reduced PM₁₀ levels (short-term exposure) were calculated using Equation (1):

$$∆y=y_0 (1-e^{-\beta ∆x})$$

(1)

where Δy represents the health benefits associated with PM₁₀ reduction (the annual reduction in deaths or hospitalizations), y₀ is the initial number of deaths or hospitalizations, β is the coefficient for the concentration–response function, and Δx is the reduction in the pollutant concentration for a specific scenario (µg/m³). The resulting Δy values were rounded to the nearest whole number to represent complete cases.

2.5. The Economic Cost Assessment

The economic assessment of the hospitalization costs due to respiratory and cardiac diseases was conducted based on the average daily cost and the mean length of hospital stays [60]. These data, derived from DATASUS, represented the average values for 2017, 2018, and 2019. The APHEKOM spreadsheet was also employed for this assessment, using the following, Equation (2):

$$C_h=V_i\times N_d\times N_c$$

(2)

where C_h is the total hospitalization cost, V_i is the daily hospitalization cost for cardiac or respiratory diseases, N_d is the average length of stay (days) for cardiac or respiratory hospitalizations, and N_c is the number of cases for cardiac or respiratory diseases attributed to PM₁₀. While V_i and N_d are obtained from DATASUS data, the number of cases due to a certain disease attributable to PM₁₀ can be found in Table 1 and Table 2 in Section 3.

2.6. Mortality Attributable to Air Pollution

The estimation of deaths attributed to PM₁₀ exposure (non-external total mortality) was conducted using the methodology proposed by Ostro et al. [64]. The average PM₁₀ concentration for 2017–2019 and the concentrations of 30 µg/m³, 20 µg/m³, and 15 µg/m³ proposed by CONAMA Resolution No. 506/2024 were used. The following, Equations (3) and (4), were applied:

$$RR=e^{(\beta \times (X-X_0)}$$

(3)

$$N_{attributable}=\left({\displaystyle \frac{RR-1}{RR}}\right)\times N_{total}$$

(4)

where RR is the relative risk, X is the annual average PM₁₀ concentration, X₀ is the concentration proposed by CONAMA, β is the concentration–response coefficient, N_attributable is the number of deaths attributed to PM₁₀, and N_total is the total number of deaths. A concentration–response coefficient (β) of 0.0008 was chosen based on the best estimate proposed by Ostro et al. [30] for regions without specific studies on this relationship in the population. The resulting N_attributable values were rounded to the nearest whole number to represent complete cases.

3. Results and Discussion

CONAMA Resolution No. 506/2024 establishes national air quality standards for PM₁₀, measured as the annual arithmetic mean of the pollutant concentration in micrograms per cubic meter (µg/m³) under conditions of 25 °C and an atmospheric pressure of 760 mmHg. The resolution outlines a five-phase timeline for implementing these standards. The first phase retains the current limit of 40 µg/m³ until 31 December 2024. The second phase sets the limit at 35 µg/m³, to be achieved by January 2025. The third phase lowers the limit to 30 µg/m³ by 2033, while the fourth phase reduces it further to 20 µg/m³ by January 2044, with an optional adjustment to 2040 or 2048. Finally, the fifth phase establishes a guideline value of 15 µg/m³, recommended by the WHO in 2021, but does not specify a target year for its implementation. For analysis purposes, this study assumed that the 15 µg/m³ limit would be reached by 2055, maintaining the 11-year interval proposed between phases 3 and 4.

First of all, the PM₁₀ concentration found in our study for 2017–2019 averaged 50.0 µg/m³ across the three sampling sites. The site-specific concentrations were 52.6 µg/m³ at Site 1, 49.9 µg/m³ at Site 2, and 47.5 µg/m³ at Site 3. These values were sufficiently similar to justify the use of an average, as no significant site-specific variations were observed in the evaluated region. Based on this average concentration, all of the results could be further calculated. Table 1 presents the number of avoidable respiratory and cardiac hospitalizations and the associated cost savings if the PM₁₀ concentration levels proposed by CONAMA for phases 3, 4, and 5 were reached in 2025. It is important to note that phases 1 and 2 are already in effect, making it unnecessary to evaluate the impacts of advancing these phases individually. However, their potential benefits are incorporated into the current evaluation of the subsequent phases, as the average PM₁₀ concentration in the city exceeds the limits established for these earlier phases. The findings demonstrate that lower pollutant concentrations result in a greater number of avoidable hospitalizations and consequently higher economic savings. Respiratory diseases exhibited a higher sensitivity to PM₁₀ reductions, as indicated by the greater number of preventable hospitalizations. However, the cost savings from respiratory and cardiac hospitalizations were similar, primarily due to the difference in the average hospitalization costs: BRL 2282.79 for cardiac cases and BRL 1478.96 for respiratory cases, according to DATASUS.

Table 1. The number of avoided cases and cost savings associated with respiratory and cardiac hospitalizations if the interim targets for PM₁₀ were advanced.

			Respiratory Hospitalizations		Cardiac Hospitalization
Scenario	Original Year	Years Advanced	Avoided	Savings (BRL)	Avoided	Savings (BRL)
30 µg/m3	2033	8	395	583,301.82	238	542,390.90
20 µg/m3	2044	19	1398	2,068,177.66	844	1,925,761.64
15 µg/m3	2055	30	2568	3,797,969.28	1551	3,540,607.29

Table 1. The number of avoided cases and cost savings associated with respiratory and cardiac hospitalizations if the interim targets for PM₁₀ were advanced.

			Respiratory Hospitalizations		Cardiac Hospitalization
Scenario	Original Year	Years Advanced	Avoided	Savings (BRL)	Avoided	Savings (BRL)
30 µg/m3	2033	8	395	583,301.82	238	542,390.90
20 µg/m3	2044	19	1398	2,068,177.66	844	1,925,761.64
15 µg/m3	2055	30	2568	3,797,969.28	1551	3,540,607.29

Table 2. Number of avoided non-external deaths associated with advancing the interim targets for the PM₁₀ concentration.

Scenario	Original Year	Years Advanced	Annual Non-External Deaths Avoided	Total Non-External Deaths Avoided
30 µg/m3	2033	8	22	173
20 µg/m3	2044	19	32	614
15 µg/m3	2055	30	38	1128

Table 2. Number of avoided non-external deaths associated with advancing the interim targets for the PM₁₀ concentration.

Scenario	Original Year	Years Advanced	Annual Non-External Deaths Avoided	Total Non-External Deaths Avoided
30 µg/m3	2033	8	22	173
20 µg/m3	2044	19	32	614
15 µg/m3	2055	30	38	1128

Critically, though superficially, extrapolating the findings from Rio Grande, a city with approximately 200,000 inhabitants and average PM₁₀ concentrations of approximately 50 µg/m³, to broader urban and industrial centers provides valuable information on the potential benefits of advancing the PM₁₀ reduction targets. To illustrate this, we provide a brief analysis of Porto Alegre, the capital of Rio Grande do Sul, with a population exceeding 1.3 million and a similar meteorological context. In this scenario, considering the same average concentration of PM₁₀ as that in Rio Grande, achieving the WHO’s guideline of 15 µg/m³ could prevent over 27,000 hospitalizations due to respiratory and cardiac diseases; save more than BRL 24.7 million in respiratory-related hospitalization costs and BRL 23 million in cardiac-related hospitalization costs; and avoid over 7000 non-external deaths. However, it is important to emphasize that the sources of emissions in Porto Alegre differ significantly from those in Rio Grande. This extrapolation is not meant to represent the actual conditions in Porto Alegre but rather to provide a hypothetical illustration of the potential benefits of advancing these targets if the pollutant levels were similar.

While these extrapolations provide a broader perspective, estimates for other localities or for the entire country are challenging to conduct at this stage due to Brazil’s highly heterogeneous pollution levels and the sparse coverage of its air quality monitoring network, which currently covers only 1.6% of its municipalities [65]. Additionally, a recent report by the Brazilian Institute of Energy and Environment revealed that only 11 of Brazil’s 27 state capitals (including the national capital Brasilia) have any form of air quality monitoring [66], further underscoring the critical gaps in data availability. This highlights the need for more robust monitoring systems and localized analyses to refine the projections.

Nonetheless, focusing on densely populated, heavily industrialized cities or cities with recognized high emissions based on other factors, where the pollutant levels may match or exceed those observed in Rio Grande, underscores the urgency of advancing the PM₁₀ targets. These regional analyses not only strengthen the argument for policy interventions but also provide more tailored approaches to guiding decision-making, emphasizing the profound benefits of improving air quality in the Brazilian context.

Air pollution in the south of Brazil has characteristics influenced by a combination of natural and anthropogenic emissions. Studies conducted in this region have shown that the particulate matter concentrations often exceed the standards recommended by the WHO and CONAMA. High levels of particulate matter have been linked to emissions from vehicular traffic, industrial activities, and urban sources, alongside the role of local meteorological conditions, such as wind speed, which can either disperse or intensify pollution levels [47,50,51,53,67].

While the potential benefits of advancing the PM₁₀ reduction targets are evident, significant barriers must be overcome for their successful implementation. Economic barriers, such as the high costs associated with transitioning to cleaner industrial processes and transportation systems, may hinder progress, particularly in regions with limited financial resources. Politically, competing priorities and resistance from stakeholders affected by stricter regulations can delay or weaken policy adoption. Logistically, the limited capacity of Brazil’s air quality monitoring network and the complexity of enforcing compliance across diverse regions present further obstacles. Overcoming these barriers requires coordinated efforts among policymakers, industry, and civil society, alongside substantial investments in monitoring infrastructure, technological innovation, and public engagement, to ensure the successful implementation of accelerated air quality improvements. Nevertheless, even advancing these targets by a few years rather than waiting the 30 years outlined in current timelines would represent a significant victory for public health and a major step forward for sustainable development in Brazil.

To the best of our knowledge, this is the first study to evaluate the economic and health impacts of advancing the targets proposed by CONAMA Resolution No. 506/2024. Its results underscore the substantial benefits of accelerating air quality improvements in industrial and urbanized cities similar to Rio Grande. The novelty of this approach lies in its ability to translate recent regulatory decisions into tangible health and economic benefits, making a compelling case for policymakers. We highlight that future studies should adopt a similar approach to guiding policy adjustments, not only considering the WHO’s guideline value as a fixed reference but also evaluating intermediate targets that are more feasible for low- and middle-income countries, where achieving the strictest standards in the short term may present significant challenges.

However, this study is subject to limitations. First, it relies on modeled scenarios that assume uniform pollutant reductions, which may not reflect localized variations in the pollution sources and meteorological conditions. Second, the analysis does not account for indirect economic benefits, such as productivity gains due to improved public health, potentially underestimating the total economic impact. Finally, the limited spatial coverage of air quality monitoring in Brazil poses challenges in applying this methodology, emphasizing the need for amore robust air quality monitoring network to improve the data acquisition and support future studies. Expanding air quality monitoring systems is critical to strengthening the foundation of informed decision-making and effective policy implementation.

In addition, applying similar methodologies to other localities requires consideration of specific contextual factors. For instance, the hospitalization data from DATASUS, while comprehensive, may have inherent limitations. Regional disparities in healthcare access could influence hospitalization rates, with under-resourced areas potentially underreporting cases due to limited healthcare access rather than lower pollution exposure. Moreover, the data may not fully account for confounding factors unrelated to PM₁₀ exposure, such as pre-existing health conditions or other environmental stressors. Similarly, the concentration–response coefficient used in this study is based on global estimates for regions without specific studies, which may not fully capture the unique health responses of the population in Rio Grande. This underscores the importance of future research to develop localized concentration–response functions that reflect the demographic, environmental, and health characteristics of the city better. Future studies should also explore the incorporation of real-time air quality monitoring data and broader spatial coverage to build on these findings.

4. Conclusions

Advancing the targets for reducing the PM₁₀ levels to 15 µg/m³ by 2025, instead of 2055, would prevent 2568 respiratory hospitalizations, 1551 cardiac hospitalizations, and 1128 deaths attributable to air pollution, resulting in a cost saving of BRL 7.3 million for the healthcare system in the city of Rio Grande alone. At the national level, these benefits become even more substantial. Although CONAMA Resolution No. 506/2024 represents progress by introducing specific interim targets, the lack of a definitive deadline for achieving the final guideline value of 15 µg/m³ limits the resolution’s overall effectiveness. The findings of this study underscore the critical need for more ambitious policies aligned with the 2021 WHO recommendations, demonstrating that accelerating air quality targets not only reduces economic costs but also significantly improves public health outcomes and life expectancy. Furthermore, expanding the air quality monitoring coverage and incorporating real-time data collection are essential to enhancing the robustness of public policies and ensuring transparency. Currently, only a fraction of Brazilian municipalities benefit from adequate air quality monitoring, leaving critical gaps in the data that hinder an accurate assessment of pollution’s health impacts and the efficacy of mitigation measures. Strengthening these systems, combined with broader regional analyses, will allow policymakers to address the diverse sources and consequences of air pollution across Brazil better. This study highlights the importance of integrated environmental strategies that prioritize both public health and economic sustainability. By advancing these efforts, Brazil can more effectively mitigate the health and economic burdens of air pollution while ensuring a healthier and more sustainable future for its population.