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Americans spend most of their time indoors at home, but comprehensive characterization of in-home air pollution is limited by the cost and size of reference-quality monitors. We assembled small “Home Health Boxes” (HHBs) to measure indoor PM2.5, PM10, CO2, CO, NO2, and O3 concentrations using filter samplers and low-cost sensors. Nine HHBs were collocated with reference monitors in the kitchen of an occupied home in Fort Collins, Colorado, USA for 168 h while wildfire smoke impacted local air quality. When HHB data were interpreted using gas sensor manufacturers' calibrations, HHBs and reference monitors (a) categorized the level of each gaseous pollutant similarly (as either low, elevated, or high relative to air quality standards) and (b) both indicated that gas cooking burners were the dominant source of CO and NO2 pollution; however, HHB and reference O3 data were not correlated. When HHB gas sensor data were interpreted using linear mixed calibration models derived via collocation with reference monitors, root-mean-square error decreased for CO2 (from 408 to 58 ppm), CO (645 to 572 ppb), NO2 (22 to 14 ppb), and O3 (21 to 7 ppb); additionally, correlation between HHB and reference O3 data improved (Pearson's r increased from 0.02 to 0.75). Mean 168-h PM2.5 and PM10 concentrations derived from nine filter samples were 19.4 μg m−3 (6.1% relative standard deviation [RSD]) and 40.1 μg m−3 (7.6% RSD). The 168-h PM2.5 concentration was overestimated by PMS5003 sensors (median sensor/filter ratio = 1.7) and underestimated slightly by SPS30 sensors (median sensor/filter ratio = 0.91).

<strong class="journal-contentHeaderColor">Abstract.</strong> Atmospheric particulate matter smaller than 2.5 <span class="inline-formula">µm</span> in diameter (PM<span class="inline-formula">_2.5</span>) has a negative impact on public health, the environment, and Earth's climate. Consequently, a need exists for accurate, distributed measurements of surface-level PM<span class="inline-formula">_2.5</span> concentrations at a global scale. Existing PM<span class="inline-formula">_2.5</span> measurement infrastructure provides broad PM<span class="inline-formula">_2.5</span> sampling coverage but does not adequately characterize community-level air pollution at high temporal resolution. This motivates the development of low-cost sensors which can be more practically deployed in spatial and temporal configurations currently lacking proper characterization. Wendt et al. (2019) described the development and validation of a first-generation device for low-cost measurement of AOD and PM<span class="inline-formula">_2.5</span>: the Aerosol Mass and Optical Depth (AMODv1) sampler. Ford et al. (2019) describe a citizen-science field deployment of the AMODv1 device. In this paper, we present an updated version of the AMOD, known as AMODv2, featuring design improvements and extended validation to address the limitations of the AMODv1 work. The AMODv2 measures AOD and PM<span class="inline-formula">_2.5</span> at 20 min time intervals. The sampler includes a motorized Sun tracking system alongside a set of four optically filtered photodiodes for semicontinuous, multiwavelength (current version at 440, 500, 675, and 870 nm) AOD sampling. Also included are a Plantower PMS5003 sensor for time-resolved optical PM<span class="inline-formula">_2.5</span> measurements and a pump/cyclone system for time-integrated gravimetric filter measurements of particle mass and composition. AMODv2 samples are configured using a smartphone application, and sample data are made available via data streaming to a companion website (<span class="uri">https://csu-ceams.com/</span>, last access: 16 July 2021). We present the results of a 9 d AOD validation campaign where AMODv2 units were co-located with an AERONET (Aerosol Robotics Network) instrument as the reference method at AOD levels ranging from 0.02 <span class="inline-formula">±</span> 0.01 to 1.59 <span class="inline-formula">±</span> 0.01. We observed close agreement between AMODv2s and the reference instrument with mean absolute errors of 0.04, 0.06, 0.03, and 0.03 AOD units at 440, 500, 675, and 870 nm, respectively. We derived empirical relationships relating the reference AOD level to AMODv2 instrument error and found that the mean absolute error in the AMODv2 deviated by less than 0.01 AOD units between clear days and elevated-AOD days and across all wavelengths. We identified bias from individual units, particularly due to calibration drift, as the primary source of error between AMODv2s and reference units. In a test of 15-month calibration stability performed on 16 AMOD units, we<span id="page6024"/> observed median changes to calibration constant values of <span class="inline-formula">−</span>7.14 %, <span class="inline-formula">−</span>9.64 %, <span class="inline-formula">−</span>0.75 %, and <span class="inline-formula">−</span>2.80 % at 440, 500, 675, and 870 nm, respectively. We propose annual recalibration to mitigate potential errors from calibration drift. We conducted a trial deployment to assess the reliability and mechanical robustness of AMODv2 units. We found that 75 % of attempted samples were successfully completed in rooftop laboratory testing. We identify several failure modes in the laboratory testing and describe design changes that we have since implemented to reduce failures. We demonstrate that the AMODv2 is an accurate, stable, and low-cost platform for air pollution measurement. We describe how the AMODv2 can be implemented in spatial citizen-science networks where reference-grade sensors are economically impractical and low-cost sensors lack accuracy and stability.

Abstract. Atmospheric particulate matter smaller than 2.5 microns in diameter (PM2.5) impacts public health, the environment, and the climate. Consequently, a need exists for accurate, distributed measurements of surface-level PM2.5 concentrations at a global scale. Remote sensing observations of aerosol optical depth (AOD) have been used to estimate surface-level PM2.5 for studies on human health and the Earth system. However, these estimates are uncertain due to a lack of measurements available to validate the derived PM2.5 products, which rely on the ratio of surface PM2.5 to AOD. Traditional monitoring of these two air quality metrics is costly and cumbersome, leading to a lack of surface monitoring networks with high spatial density. In part 1 of this series we described the development and validation of a first-generation device for low-cost measurement of AOD and PM2.5: The Aerosol Mass and Optical Depth (AMODv1) sampler. Part 2 of the series describes a citizen-science field deployment of the AMODv1 device. Here in part 3, we present an autonomous version of the AMOD, known as AMODv2, capable of unsupervised measurement of AOD and PM2.5 at 20-minute time intervals. The AMODv2 includes a set of four optically filtered photodiodes for multi-wavelength (current version at 440, 500, 675, and 870 nm) AOD, a Plantower PMS5003 sensor for time-resolved optical PM2.5 measurements, and a pump and cyclone system for time-integrated gravimetric filter measurements of particle mass and composition. The AMODv2 uses low-cost motors and sensor data for autonomous sun alignment to provide the semi-continuous AOD measurements. Operators can connect to the AMODv2 over Bluetooth® and configure a sample using a smartphone application. A Wi-Fi module enables real-time data streaming and visualization on our website (csu-ceams.com). We present a sample deployment of 10 AMODv2s during a wildfire smoke event and demonstrate the ability of the instrument to capture changes in air quality at sub-hourly time resolution. We also present the results of an AOD validation campaign where AMODv2s were co-located with AERONET (Aerosol Robotics Network) instruments as the reference method at AOD levels ranging from 0.016 to 1.59. We observed close agreement between AMODv2s and the reference instrument with mean absolute errors of 0.046, 0.057, 0.026, and 0.033 AOD units at 440 nm, 500 nm, 675 nm, and 870 nm, respectively. We identified individual unit bias as the primary source of error between AMODv2s and reference units and propose re-calibration to mitigate these biases. The AMODv2 is well suited for citizen-science and other high-spatial-density deployments due to its low cost, compact form, user-friendly interface, and high measurement frequency of AOD and PM2.5. These deployments could provide a rich air pollution data set for evaluating remote sensing observations, atmospheric modeling simulations, and provide communities with the information they need to implement effective public health and environmental interventions.

Pathogen detection is crucial for human, animal, and environmental health; crop protection; and biosafety. Current culture-based methods have long turnaround times and lack sensitivity. Nucleic acid amplification tests offer high specificity and sensitivity. However, their cost and complexity remain a significant hurdle to their applications in resource-limited settings. Thus, point-of-need molecular diagnostic platforms that can be used by minimally trained personnel are needed. The nuclease protection assay (NPA) is a nucleic acid hybridization-based technique that does not rely on amplification, can be paired with other methods to improve specificity, and has the potential to be developed into a point-of-need device. In traditional NPAs, hybridization of an anti-sense probe to the target sequence is followed by single-strand nuclease digestion. The double-stranded target-probe hybrids are protected from nuclease digestion, precipitated, and visualized using autoradiography or other methods. We have developed a paper-based nuclease protection assay (PB-NPA) that can be implemented in field settings as the detection approach requires limited equipment and technical expertise. The PB-NPA uses a lateral flow format to capture the labeled target-probe hybrids onto a nitrocellulose membrane modified with an anti-label antibody. A colorimetric enzyme-substrate pair is used for signal visualization, producing a test line. The nuclease digestion of non-target and mismatched DNA provides high specificity while signal amplification with the reporter enzyme-substrate provides high sensitivity. We have also developed an on-chip sample pretreatment step utilizing chitosan-modified paper to eliminate possible interferents from the reaction and preconcentrate nucleic acids, thereby significantly reducing the need for auxiliary equipment. Graphical abstract.

Abstract. Globally, fine particulate matter (PM2.5) air pollution is a leading contributor to death, disease, and environmental degradation. Satellite-based measurements of aerosol optical depth (AOD) are used to estimate PM2.5 concentrations across the world, but the relationship between satellite-estimated AOD and ground-level PM2.5 is uncertain. Sun photometers measure AOD from the Earth's surface and are often used to improve satellite data; however, reference-grade photometers and PM2.5 monitors are expensive and rarely co-located. This work presents the development and validation of the aerosol mass and optical depth (AMOD) sampler, an inexpensive and compact device that simultaneously measures PM2.5 mass and AOD. The AMOD utilizes a low-cost light-scattering sensor in combination with a gravimetric filter measurement to quantify ground-level PM2.5. Aerosol optical depth is measured using optically filtered photodiodes at four discrete wavelengths. Field validation studies revealed agreement within 10 % for AOD values measured between co-located AMOD and AErosol RObotics NETwork (AERONET) monitors and for PM2.5 mass measured between co-located AMOD and EPA Federal Equivalent Method (FEM) monitors. These results demonstrate that the AMOD can quantify AOD and PM2.5 accurately at a fraction of the cost of existing reference monitors.

Metal contamination of natural and drinking water systems poses hazards to public and environmental health. Quantifying metal concentrations in water typically requires sample collection in the field followed by expensive laboratory analysis that can take days to weeks to obtain results. The objective of this work was to develop a low-cost, field-deployable method to quantify trace levels of copper in drinking water by coupling solid-phase extraction/preconcentration with a microfluidic paper-based analytical device. This method has the advantages of being hand-powered (instrument-free) and using a simple "read by eye" quantification motif (based on color distance). Tap water samples collected across Fort Collins, CO, were tested with this method and validated against ICP-MS. We demonstrate the ability to quantify the copper content of tap water within 30% of a reference technique at levels ranging from 20 to 500 000 ppb. The application of this technology, which should be sufficient as a rapid screening tool, can lead to faster, more cost-effective detection of soluble metals in water systems.

The field of paper-based microfluidics has experienced rapid growth over the past decade. Microfluidic paper-based analytical devices (μPADs), originally developed for point-of-care medical diagnostics in resource-limited settings, are now being applied in new areas, such as environmental analyses. Low-cost paper sensors show great promise for on-site environmental analysis; the theme of ongoing research complements existing instrumental techniques by providing high spatial and temporal resolution for environmental monitoring. This review highlights recent applications of μPADs for environmental analysis along with technical advances that may enable μPADs to be more widely implemented in field testing.