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Accurately predicting urban PM2.5 concentrations and composition has proved challenging in the past, partially due to the resolution limitations of computationally intensive chemical transport models (CTMs). Increasing the resolution of PM2.5 predictions is desired to support emissions control policy development and address issues related to environmental justice. A nested grid approach using the CTM PMCAMx-v2.0 was used to predict PM2.5 at increasing resolutions of 36 km × 36 km , 12 km × 12 km , 4 km × 4 km , and 1 km × 1 km for a domain largely consisting of Allegheny County and the city of Pittsburgh in southwestern Pennsylvania, US, during February and July 2017. Performance of the model in reproducing PM2.5 concentrations and composition was evaluated at the finest scale using measurements from regulatory sites as well as a network of low-cost monitors. Novel surrogates were developed to allocate emissions from cooking and on-road traffic sources to the 1 km × 1 km resolution grid. Total PM2.5 mass is reproduced well by the model during the winter period with low fractional error (0.3) and fractional bias (+ 0.05) when compared to regulatory measurements. Comparison with speciated measurements during this period identified small underpredictions of PM2.5 sulfate, elemental carbon (EC), and organic aerosol (OA) offset by a larger overprediction of PM2.5 nitrate. In the summer period, total PM2.5 mass is underpredicted due to a large underprediction of OA (bias = - 1.9 µgm-3 , fractional bias = - 0.41). In the winter period, the model performs well in reproducing the variability between urban measurements and rural measurements of local pollutants such as EC and OA. This effect is less consistent in the summer period due to a larger fraction of long-range-transported OA. Comparison with total PM2.5 concentration measurements from low-cost sensors showed improvements in performance with increasing resolution. Inconsistencies in PM2.5 nitrate predictions in both periods are believed to be due to errors in partitioning between PM2.5 and PM10 modes and motivate improvements to the treatment of dust particles within the model. The underprediction of summer OA would likely be improved by updates to biogenic secondary organic aerosol (SOA) chemistry within the model, which would result in an increase of long-range transport SOA seen in the inner modeling domain. These improvements are obvious topics for future work towards model improvement. Comparison with regulatory monitors showed that increasing resolution from 36 to 1 km improved both fractional error and fractional bias in both modeling periods. Improvements at all types of measurement locations indicated an improved ability of the model to reproduce urban–rural PM2.5 gradients at higher resolutions. [ABSTRACT FROM AUTHOR]

Accurate predictions of source resolved atmospheric PM 2.5 concentrations at high resolutions using chemical transport models (CTMs) require expensive CTM simulations and development of high-resolution emissions inventories. We use multiple machine learning (ML) approaches to downscale coarse-resolution (36 × 36 km2) CTM predictions to 1 × 1 km2 spatial resolution. ML predictions include concentrations of the major chemical components of PM 2.5 and the contributions of its major emissions sources. Inputs for the ML models include 36 × 36 km2 source resolved CTM predicted concentrations of all PM 2.5 components, meteorological data, and several land-use (LU) variables. The output of our ML models is the 1 × 1 km2 source-resolved concentrations of all major PM 2.5 components in southwestern Pennsylvania (5184 km2 domain) during February and July 2017. Models were trained and validated using 1 × 1 km2 resolution source- and species-resolved CTM predictions of PM 2.5 from recent complementary studies. The best overall performance was found using a random forest (RF) model, where species and source resolved PM 2.5 concentrations were reproduced with low normalized mean bias (|NMB| < 0.01). The downscaling model captures the spatial distribution of PM 2.5 both by component and source, with some discrepancies when predicting the plumes of large point sources that have long-range impacts. In a test of generalizability to unknown domains, the model differentiates well between areas that are primarily urban, rural, or industrial but faces challenges with the reproduction of the effects of large point sources of PM 2.5 when entire quadrants are removed from the training data. The results represent a proof of concept for downscaling low-resolution CTM predictions using native high-resolution CTM predictions in training. • New ML method for downscaling chemical transport model predictions. • Provides particle mass concentration, composition, and source contributions. • Does not require high-resolution emission inventories. • Uses as inputs available land use regression (LUR) variables. • Encouraging results in its first application in Pittsburgh, US. [ABSTRACT FROM AUTHOR]

Objectives: With increased focus on early resuscitation methods following injury to improve patient outcomes, studies are employing exception from informed consent (EFIC) enrollment. Few studies have assessed patients' opinions following participation in an EFIC study, and none have been conducted within the realm of traumatic hemorrhage. We surveyed those patients and surrogates previously enrolled in the Prehospital Air Medical Plasma (PAMPer) Trial to clarify their opinions related to consent and emergency research., Methods: Telephone calls were made between January-June 2019 to all patients who were enrolled under EFIC in the PAMPer study at the Pittsburgh site (169 of the 501 total patients enrolled, May 2014-Oct 2017) and their surrogates. Questions gauging approval of EFIC enrollment were asked before discussion of PAMPer trial outcomes, after disclosure of positive outcomes, and after a hypothetical negative trial outcome was proposed., Results: Of the total 647 telephone calls made, ninety-three interviews, reflecting 70 of 169 patient enrollments, were conducted. This included 13 in which only the patient was interviewed, 23 in which the patient and a surrogate were interviewed, and 34 in which only a surrogate was interviewed. Nearly half (48.4%) of respondents did not recall their personal or family member enrollment in the study. No patients or surrogates recalled hearing about the study through community consultation or being aware of opt out procedures. Patients and surrogates were glad they were enrolled (90.3%), agreed with EFIC use for their personal enrollment (88.17%), and agreed with the general use of EFIC for the PAMPer study (81.7%). Disclosure of the true positive PAMPer study outcome resulted in a significant increase in opinions regarding personal enrollment, EFIC for personal enrollment, and EFIC for general enrollment (all p < 0.001). Disclosure of a hypothetical neutral or negative study outcome resulted in significant decreases in opinions regarding EFIC for personal enrollment (p = 0.003) and EFIC for general enrollment (p < 0.001)., Conclusions: Clinical trial participants with traumatic hemorrhagic shock enrolled with EFIC, and surrogates of such participants, are generally accepting of EFIC. The results of the trial in which EFIC was utilized significantly affected patient and surrogate agreement with personal and general EFIC enrollment.

The epidemiological evidence for ultrafine particles (UFP; particles with diameter <100 nm) causing chronic health effects independent of fine particulate matter (PM 2.5 ) mass is inconclusive. A prevailing view is that urban UFP and PM 2.5 mass have different spatial patterns, which should allow epidemiological studies to distinguish their independent, chronic health effects. We investigate intraurban spatial correlation of PM 2.5 and UFP exposures in Pittsburgh, Pennsylvania. Measurements and predictions of a land-use regression model indicate moderate spatial correlation between particle number concentrations (PNC; a proxy for UFP) and PM 2.5 ( R 2 of 0.38 and 0.41, respectively). High-resolution (1-km) chemical transport model simulations predict stronger spatial correlation ( R 2 ≈ 0.8). The finding of moderate to strong spatial correlation was initially surprising because secondary aerosol contributes the vast majority of PM 2.5 mass. However, intraurban spatial patterns of both PNC and PM 2.5 are driven by local emissions and both pollutants largely behave as passive tracers at time scales of 1 day or less required for transport across most urban environments. Although previous research has shown little temporal correlation between PNC and PM 2.5 , our finding of moderate to strong spatial correlation may complicate epidemiological analyses to separate the chronic health effects of PNC from PM 2.5 mass.