Abstract. Low-cost particulate mass sensors provide opportunities to assess air quality at unprecedented spatial and temporal resolutions. Established traditional monitoring networks have limited spatial resolution and are simply absent in many major cities across sub-Saharan Africa (SSA). Satellites provide snapshots of regional air pollution but require ground-truthing. Low-cost monitors can supplement and extend data coverage from these sources worldwide, providing a better overall air quality picture. We investigate the utility of such a multi-source data integration approach using two case studies. First, in Pittsburgh, Pennsylvania, both traditional monitoring and dense low-cost sensor networks are compared with satellite aerosol optical depth (AOD) data from NASA's MODIS system, and a linear conversion factor is developed to convert AOD to surface fine particulate matter mass concentration (as PM2.5). With 10 or more ground monitors in Pittsburgh, there is a 2-fold reduction in surface PM2.5 estimation mean absolute error compared to using only a single ground monitor. Second, we assess the ability of combined regional-scale satellite retrievals and local-scale low-cost sensor measurements to improve surface PM2.5 estimation at several urban sites in SSA. In Rwanda, we find that combining local ground monitoring information with satellite data provides a 40 % improvement in surface PM2.5 estimation accuracy with respect to using low-cost ground monitoring data alone. A linear AOD-to-surface-PM2.5 conversion factor developed in Kigali, Rwanda, did not generalize well to other parts of SSA and varied seasonally for the same location, emphasizing the need for ongoing and localized ground-based monitoring, which can be facilitated by low-cost sensors. Overall, we find that combining ground-based low-cost sensor and satellite data, even without including additional meteorological or land use information, can improve and expand spatiotemporal air quality data coverage, especially in data-sparse regions.

National governments and other key stakeholders in developing countries are grappling with how to reduce household air pollution (HAP) resulting from cooking with solid fuels using traditional cooking technologies. Recent studies have shown that improved cookstoves may offer reductions in fuel use and harmful emissions of carbon monoxide (CO) and fine particulate matter (PM2.5), yet there is little quantitative evidence collected in a “real-world” setting showing how improved stoves perform directly compared to traditional cooking technologies. Our simulated kitchen study takes place in a semi-controlled, “real-world” setting in Malawi and was designed to quantify the fuel efficiency improvements and air pollutant concentration reductions capabilities of two improved stoves currently marketed in the country. In this study, we perform a Water Boiling Test (WBT) to compare the air pollutant concentrations (CO and PM2.5) and fuel efficiency of the traditional three-stone fire stove and two improved cookstoves: a locally produced clay stove known as the Chitetezo Mbaula (CM) and a Philips gasifying stove. We find that the Chitetezo Mbaula uses 53% of the fuel used by the traditional three-stone fire, and produces 59% of CO, and 50% of PM2.5 of the three-stone fire. The Philips gasifying stove uses 31% of the fuel, and produces 38% of CO, and 22% of PM2.5 of the traditional three-stone fire. We consider the potential for the wide-scale adoption of each of these technologies given their relative costs and conclude that lower-cost, intermediate quality cookstoves are an important and realistic first step toward reducing household air pollution.

We identify and characterize compact dwarf starburst (CDS) galaxies in the RE-SOLVE survey, a volume-limited census of galaxies in the local universe, to probe whether this population contains any residual "blue nuggets," a class of intensely starforming compact galaxies first identified at high redshift z. Our 50 low-z CDS galaxies are defined by dwarf masses (stellar mass M * < 10 9.5 M ), compact bulged-disk or spheroid-dominated morphologies (using a quantitative criterion, µ ∆ > 8.6), and specific star formation rates above the defining threshold for high-z blue nuggets (log SSFR [Gyr −1 ] > −0.5). Across redshifts, blue nuggets exhibit three defining properties: compactness relative to contemporaneous galaxies, abundant cold gas, and formation via compaction in mergers or colliding streams. Those with halo mass below M halo ∼ 10 11.5 M may in theory evade permanent quenching and cyclically refuel until the present day. Selected only for compactness and starburst activity, our CDS galaxies generally have M halo 10 11.5 M and gas-to-stellar mass ratio 1. Moreover, analysis of archival DECaLS photometry and new 3D spectroscopic observations for CDS galaxies reveals a high rate of photometric and kinematic disturbances suggestive of dwarf mergers. The SSFRs, surface mass densities, and number counts of CDS galaxies are compatible with theoretical and observational expectations for redshift evolution in blue nuggets. We argue that CDS galaxies represent a maximally-starbursting subset of traditional compact dwarf classes such as blue compact dwarfs and blue E/S0s. We conclude that CDS galaxies represent a low-z tail of the blue nugget phenomenon formed via a moderated compaction channel that leaves open the possibility of disk regrowth and evolution into normal disk galaxies.

Existing star-forming vs. active galactic nucleus (AGN) classification schemes using optical emission-line diagnostics mostly fail for low-metallicity and/or highly star-forming galaxies, missing AGN in typical z ∼ 0 dwarfs. To recover AGN in dwarfs with strong emission lines (SELs), we present a classification scheme optimizing the use of existing optical diagnostics. We use Sloan Digital Sky Survey emission-line catalogs overlapping the volume- and mass-limited REsolved Spectroscopy Of a Local VolumE (RESOLVE) and Environmental COntex (ECO) surveys to determine the AGN percentage in SEL dwarfs. Our photoionization grids show that the [O iii]/Hβ versus [S ii]/Hα diagram (S ii plot) and [O iii]/Hβ versus [O i]/Hα diagram (O i plot) are less metallicity sensitive and more successful in identifying dwarf AGN than the popular [O iii]/Hβ versus [N ii]/Hα diagnostic (N ii plot or “BPT diagram”). We identify a new category of “star-forming AGN” (SF-AGN) classified as star-forming by the N ii plot but as AGN by the S ii and/or O i plots. Including SF-AGN, we find the z ∼ 0 AGN percentage in dwarfs with SELs to be ∼3%–16%, far exceeding most previous optical estimates (∼1%). The large range in our dwarf AGN percentage reflects differences in spectral fitting methodologies between catalogs. The highly complete nature of RESOLVE and ECO allows us to normalize strong emission-line galaxy statistics to the full galaxy population, reducing the dwarf AGN percentage to ∼0.6%–3.0%. The newly identified SF-AGN are mostly gas-rich dwarfs with halo mass <1011.5 M ⊙, where highly efficient cosmic gas accretion is expected. Almost all SF-AGN also have low metallicities (Z ≲ 0.4 Z ⊙), demonstrating the advantage of our method.

Abstract. Low-cost gas and particulate matter sensor packages offer a compact, lightweight, and easily transportable solution to address global gaps in air quality (AQ) observations. However, regions that would benefit most from widespread deployment of low-cost AQ monitors often lack the reference-grade equipment required to reliably calibrate and validate them. In this study, we explore approaches to calibrating and validating three integrated sensor packages before a 1-year deployment to rural Malawi using colocation data collected at a regulatory site in North Carolina, USA. We compare the performance of five computational modeling approaches to calibrate the electrochemical gas sensors: k-nearest neighbors (kNN) hybrid, random forest (RF) hybrid, high-dimensional model representation (HDMR), multilinear regression (MLR), and quadratic regression (QR). For the CO, Ox, NO, and NO2 sensors, we found that kNN hybrid models returned the highest coefficients of determination and lowest error metrics when validated. Hybrid models were also the most transferable approach when applied to deployment data collected in Malawi. We compared kNN hybrid calibrated CO observations from two regions in Malawi to remote sensing data and found qualitative agreement in spatial and annual trends. However, ARISense monthly mean surface observations were 2 to 4 times higher than the remote sensing data, partly due to proximity to residential biomass combustion activity not resolved by satellite imaging. We also compared the performance of the integrated Alphasense OPC-N2 optical particle counter to a filter-corrected nephelometer using colocation data collected at one of our deployment sites in Malawi. We found the performance of the OPC-N2 varied widely with environmental conditions, with the worst performance associated with high relative humidity (RH >70 %) conditions and influence from emissions from nearby residential biomass combustion. We did not find obvious evidence of systematic sensor performance decay after the 1-year deployment to Malawi. Data recovery (30 %–80 %) varied by sensor and season and was limited by insufficient power and access to resources at the remote deployment sites. Future low-cost sensor deployments to rural, low-income settings would benefit from adaptable power systems, standardized sensor calibration methodologies, and increased regional regulatory-grade monitoring infrastructure.