Background and Objectives: Numerous studies suggest that environmental exposures play a critical role in Parkinson disease (PD) pathogenesis, and large, population-based studies have the potential to advance substantially the identification of novel PD risk factors. We sought to study the nationwide geographic relationship between PD and air pollution, specifically PM_2.5 (particulate matter with a diameter <2.5 micrometers), using population-based, United States (U.S.) Medicare data. Methods: We conducted a population-based geographic study of Medicare beneficiaries aged 66-90 geocoded to U.S. counties and zip+4. We used integrated nested Laplace approximation to create age, sex, race, smoking, and healthcare utilization adjusted relative risk (RR) at the county-level for geographical analyses with PM_2.5 as the primary exposure of interest. We also performed an individual-level analysis using logistic regression with cases and controls with zip+4 centroid PM_2.5. We adjusted a priori for the same covariates and verified no confounding by indicators of socioeconomic status or neurologist density. Results: Among 21,639,190 Medicare beneficiaries, 89,390 had incident PD in 2009. There was a nationwide association between average annual PM_2.5 and PD risk whereby the RR for PD was 56% (95% CI 47%-66%) greater for those exposed to the median level of PM_2.5 compared to those with the lowest level of PM_2.5. This association was linear up to 13 μg/m³ corresponding to a 4.2% (95% CI 3.7%-4.8%) greater risk of PD for each additional μg/m³ of PM_2.5 (p_trend<0.0001). We identified a region with high PD risk in the Mississippi-Ohio River Valley, where the risk for PD was 19% greater compared to the rest of the nation. The strongest association between PM_2.5 and PD was found in a region with low PD risk in the Rocky Mountains. PM_2.5 was also associated with PD in the Mississippi-Ohio River Valley where the association was relatively weaker, due to a possible ceiling effect at average annual PM_2.5 levels of ∼13 μg/m³. Discussion: State-of-the-art geographic analytic techniques revealed an association between PM_2.5 and PD that varied in strength by region. A deeper investigation into the specific subfractions of PM_2.5 may provide additional insight into regional variability in the PM_2.5-PD association.

Background: Exposure to occupational manganese (Mn) is associated with neurotoxic brain injury, manifesting primarily as parkinsonism. The association between environmental Mn exposure and parkinsonism is unclear. To characterize the association between environmental Mn exposure and parkinsonism, we performed population-based sampling of residents older than 40 in Meyerton, South Africa (N = 621) in residential settlements adjacent to a large Mn smelter and in a comparable non-exposed settlement in Ethembalethu, South Africa (N = 95) in 2016–2020. Methods: A movement disorders specialist examined all participants using the Unified Parkinson Disease Rating Scale motor subsection part 3 (UPDRS3). Participants also completed an accelerometry-based kinematic test and a grooved pegboard test. We compared performance on the UPDRS3, grooved pegboard, and the accelerometry-based kinematic test between the settlements using linear regression, adjusting for covariates. We also measured airborne PM_2.5-Mn in the study settlements. Results: Mean PM_2.5-Mn concentration at a long-term fixed site in Meyerton was 203 ng/m³ in 2016–2017 – approximately double that measured at two other neighborhoods in Meyerton. The mean Mn concentration in Ethembalethu was ~ 20 times lower than that of the long-term Meyerton site. UPDRS3 scores were 6.6 (CI 5.2, 7.9) points higher in Meyerton than Ethembalethu residents. Mean angular velocity for finger-tapping on the accelerometry-based kinematic test was slower in Meyerton than Ethembalethu residents [dominant hand 74.9 (CI 48.7, 101.2) and non-dominant hand 82.6 (CI 55.2, 110.1) degrees/second slower]. Similarly, Meyerton residents took longer to complete the grooved pegboard, especially for the non-dominant hand (6.9, CI -2.6, 16.3 s longer). Conclusions: Environmental airborne Mn exposures at levels substantially lower than current occupational exposure thresholds in the United States may be associated with clinical parkinsonism.

A robust method to estimate mineral dust mass in ambient particulate matter (PM) is essential, as the dust fraction cannot be directly measured but is needed to understand dust impacts on the environment and human health. In this study, a global‐scale dust equation is developed that builds on the widely used Interagency Monitoring of Protected Visual Environments (IMPROVE) network's “soil” formula that is based on five measured elements (Al, Si, Ca, Fe, and Ti). We incorporate K, Mg, and Na into the equation using the mineral‐to‐aluminum (MAL) mass ratio of (K₂O + MgO + Na₂O)/Al₂O₃ and apply a correction factor (CF) to account for other missing compounds. We obtain region‐specific MAL ratios and CFs by investigating the variation in dust composition across desert regions. To calculate reference dust mass for equation evaluation, we use total‐mineral‐mass (summing all oxides of crustal elements) and residual‐mass (subtracting non‐dust species from total PM) approaches. For desert dust in source regions, the normalized mean bias (NMB) of the global equation (within ±1%) is significantly smaller than the NMB of the IMPROVE equation (−6% to 10%). For PM_2.5 with high dust content measured by the IMPROVE network, the global equation estimates dust mass well (NMB within ±5%) at most sites. For desert dust transported to non‐source regions, the global equation still performs well (NMB within ±2%). The global equation can also represent paved road, unpaved road, and agricultural soil dust (NMB within ±5%). This global equation provides a promising approach for calculating dust mass based on elemental analysis of dust.

In the United States, citizens and policymakers heavily rely upon Environmental Protection Agency mandated regulatory networks to monitor air pollution; increasingly they also depend on low‐cost sensor networks to supplement spatial gaps in regulatory monitor networks coverage. Although these regulatory and low‐cost networks in tandem provide enhanced spatiotemporal coverage in urban areas, low‐cost sensors are located often in higher income, predominantly White areas. Such disparity in coverage may exacerbate existing inequalities and impact the ability of different communities to respond to the threat of air pollution. Here we present a study using cost‐constrained multiresolution dynamic mode decomposition (mrDMDcc) to identify the optimal and equitable placement of fine particulate matter (PM_2.5) sensors in four U.S. cities with histories of racial or income segregation: St. Louis, Houston, Boston, and Buffalo. This novel approach incorporates the variation of PM_2.5 on timescales ranging from 1 day to over a decade to capture air pollution variability. We also introduce a cost function into the sensor placement optimization that represents the balance between our objectives of capturing PM_2.5 extremes and increasing pollution monitoring in low‐income and nonwhite areas. We find that the mrDMDcc algorithm places a greater number of sensors in historically low‐income and nonwhite neighborhoods with known environmental pollution problems compared to networks using PM_2.5 information alone. Our work provides a roadmap for the creation of equitable sensor networks in U.S. cities and offers a guide for democratizing air pollution data through increasing spatial coverage of low‐cost sensors in less privileged communities.