Atmospheric particulate matter (PM) has the potential to diminish solar energy production by direct and indirect radiative forcing as well as by being deposited on solar panel surfaces, thereby reducing solar energy transmittance to photovoltaics. Worldwide solar energy production is expected to increase more rapidly than any other energy source into the middle of this century, especially in regions that experience high levels of dust and/or anthropogenic particulate pollutants, including large areas of India, China, and the Arabian Peninsula. Here we combine field measurements and global modeling to estimate the influence of dust and PM related to anthropogenic sources (e.g., fossil and biomass fuel combustion) on solar electricity generation. Results indicate that solar energy production is currently reduced by ∼17–25% across these regions, with roughly equal contributions from ambient PM and PM deposited on photovoltaic surfaces. Reductions due to dust and anthropogenic PM are comparable in northern India, whereas over eastern China, anthropogenic PM dominates. On the basis of current solar generation capacity, PM is responsible for ∼1 and ∼11 GW of solar power reduction in India and China, respectively, underscoring the large role that PM plays in reducing solar power generation output.

The international journal of Aerosol and Air Quality Research (AAQR) covers all aspects of aerosol science and technology, atmospheric science and air quality related issues.

Railyards are important transportation hubs, and they are often situated near populated areas with high co-located density of manufacturing, freight movement and commercial enterprises. Emissions occurring within railyards can affect nearby air quality. To better understand the air pollution levels in proximity to a major railyard, an intensive mobile air monitoring study was conducted in May 2012 around a major railyard area in Atlanta, GA, constituted of two separate facilities situated side-by-side. A total of 19 multi-hour mobile monitoring sessions took place over different times of day, days of the week, and under a variety of wind conditions. High time resolution measurements included black carbon (BC), particle number concentration (PN), particle optical extinction (EXT), oxides of nitrogen (NO, NO₂, NOy), carbon monoxide (CO), and speciated air toxics. Urban background was estimated to contribute substantially (>70%) to EXT and CO, whereas BC, oxides of nitrogen (NOx) and toluene had comparably low background contributions (<30%). Mobile monitoring data were aggregated into 50 meter spatial medians by wind categories, with categories including low speed wind conditions (<0.5 m s⁻¹) and, for wind speeds above that threshold, by wind direction relative to the railyard. Spatial medians of different pollutants measured had a wide range of correlation—gas-phase air toxics (benzene, toluene, acetaldehyde) had moderate correlation with each other (r = 0.46–0.59) and between toluene and CO (r = 0.53), but lower correlation for other pairings. PN had highest correlation with oxides of nitrogen (r = 0.55–0.66), followed by BC (r = 0.4), and lower correlation with other pollutants. Multivariate regression analysis on the full set of 50 m medians found BC and NO as having the strongest relationship to railyard emissions, in comparison to their respective background levels. This was indicated by an increase associated with transiting through the yard and inverse relationship with distance from the railyard; NO and BC decreased by a factor of approximately 0.5 and 0.7 over 1 km distance of the railyard boundary, respectively. Low speed, variable wind conditions were related to higher concentrations of all measured parameters.

Satellite-based rapid sweeping screening of localized PM_2.5 hotspots at fine-scale local neighborhood levels is highly desirable. This motivated us to develop a random forest–convolutional neural network–local contrast normalization (RF–CNN–LCN) pipeline that detects local PM_2.5 hotspots at a 300 m resolution using satellite imagery and meteorological information. The RF–CNN joint model in the pipeline uses three meteorological variables and daily 3 m/pixel resolution PlanetScope satellite imagery to generate daily 300 m ground-level PM_2.5 estimates. The downstream LCN processes the estimated PM_2.5 maps to reveal local PM_2.5 hotspots. The RF–CNN joint model achieved a low normalized root mean square error for PM_2.5 of within ~31% and normalized mean absolute error of within ~19% on the holdout samples in both Delhi and Beijing. The RF–CNN–LCN pipeline reasonably predicts urban PM_2.5 local hotspots and coolspots by capturing both the main intra-urban spatial trends in PM_2.5 and the local variations in PM_2.5 with urban landscape, with local hotspots relating to compact urban spatial structures and coolspots being open areas and green spaces. Based on 20 sampled representative neighborhoods in Delhi, our pipeline revealed an annual average 9.2 ± 4.0 μg m⁻³ difference in PM_2.5 between the local hotspots and coolspots within the same community. In some cases, the differences were much larger; for example, at the Indian Gandhi International Airport, the increase was 20.3 μg m⁻³ from the coolest spot (the residential area immediately outside the airport) to the hottest spot (airport runway). This work provides a possible means of automatically identifying local PM_2.5 hotspots at 300 m in heavily polluted megacities and highlights the potential existence of substantial health inequalities in long-term outdoor PM_2.5 exposures even within the same local neighborhoods between local hotspots and coolspots.