Accurate measurement of aerosol light absorption is essential to reduce uncertainties associated with aerosol radiative forcing in climate models. Contemporary techniques to measure aerosol light absorption continue to suffer from systematic errors and biases that are difficult to quantify. Photoacoustic spectrometry offers a first-principles approach for in-situ measurement of aerosol light absorption. The photoacoustic effect takes place when particles are illuminated by an amplitude-modulated laser, whereupon the particles absorb some amount of incident laser beam energy. Most of the absorbed energy, typically, is released to the surrounding gas as heat, creating pressure waves (sound) of an intensity proportional to that of the modulated laser power. These pressure waves are detected using a sensitive acoustic sensor in real time. This tutorial outlines the working principle of a basic photoacoustic spectrometer (PAS) from a fundamental standpoint, and elucidates its construction by taking into consideration the different design constraints and optical configurations. Methods for data acquisition and signal processing are explained in detail. The tutorial concludes with a brief discussion on PAS calibration techniques, determination of the instrument's limit of detection, and the intrinsic limitations of the technology.

This study focuses on filter-based aerosol light absorption measurement biases and their correction algorithms in the coastal urban-industrial area of Houston-Galveston region. Known as a major petrochemical hub in the United States, this area is dominated by industrial flaring emissions. The aerosols in this region are mainly composed of organic carbon and sulfates. We observed that conventional filter-based instruments, despite their cost-effectiveness and simplicity of use, overestimate aerosol light absorption by approximately four times in comparison to reference particle phase instruments, such as photoacoustic spectrometers. To mitigate these unquantifiable measurement biases, we applied and compared different analytical correction algorithms including the widely used Bond-Ogren (2010) and Virkkula (2010), as well as a customized Random Forest Regression (RFR) machine learning algorithm. Our analysis revealed that RFR significantly improved correction efficacy, reducing the wavelength-averaged Root Mean Square Error (RMSE) by approximately 50% compared to traditional analytical methods. We performed SHapley Additive exPlanations (SHAP) analysis to identify the key parameters that influence the accuracy of our RFR correction algorithm. We find that at longer visible wavelengths, dark-brown carbon from flaring emissions in the sampling region exacerbates biases in filter-based measurements. This study underscores the importance of employing advanced correction algorithms for correcting filter-based aerosol light absorption measurements, especially in complex urban settings influenced by industrial emissions.

Organic and elemental carbon (OC and EC) are operationally-defined by the measurement process, so long-term trends may be interrupted with instrumentation changes. A modification to the U.S. IMPROVE carbon analysis protocol and hardware is examined that replaces the 633 nm laser light used for OC charring adjustments with seven wavelengths ranging from 405 to 980 nm, including one at 635 nm. Reflectance (R) and Transmittance (T) values for each wavelength are made traceable to primary standards through transfer standards consisting of a range of aerosol deposits on filter media similar to that of the analyzed samples. R and T values are assigned to these filters using a UV/VIS spectrometer calibrated with these standards. Using ambient and source (e.g., diesel exhaust, flaming biomass, and smoldering biomass) samples, it is demonstrated that R and T calibration is independent of the sample type. Total carbon (TC), OC, and EC comparisons with the earlier hardware design for urban- and non-urban samples demonstrate equivalence, within precisions derived from replicate analyses, for the 633 nm and 635 nm wavelengths. Several uses of the additional multiwavelength information are identified, including: 1) ground-truthing of multi-spectral remote sensors; 2) improving estimates of the Earth's radiation balance; 3) associating specific organic compounds with their light absorption properties; and 4) appropriating sources of black and brown carbon.

Wildfires emit large amounts of black carbon and light-absorbing organic carbon, known as brown carbon, into the atmosphere. These particles perturb Earth's radiation budget through absorption of incoming shortwave radiation. It is generally thought that brown carbon loses its absorptivity after emission in the atmosphere due to sunlight-driven photochemical bleaching. Consequently, the atmospheric warming effect exerted by brown carbon remains highly variable and poorly represented in climate models compared with that of the relatively nonreactive black carbon. Given that wildfires are predicted to increase globally in the coming decades, it is increasingly important to quantify these radiative impacts. Here we present measurements of ensemble-scale and particle-scale shortwave absorption in smoke plumes from wildfires in the western United States. We find that a type of dark brown carbon contributes three-quarters of the short visible light absorption and half of the long visible light absorption. This strongly absorbing organic aerosol species is water insoluble, resists daytime photobleaching and increases in absorptivity with night-time atmospheric processing. Our findings suggest that parameterizations of brown carbon in climate models need to be revised to improve the estimation of smoke aerosol radiative forcing and associated warming.

Global radiative forcing by carbonaceous aerosols is one of the largest sources of uncertainty in current climate models. The radiative impacts of brown carbon (BrC), a type of amorphous organic carbonaceous aerosol formed in biomass burning events, such as wildfires, remains poorly understood. Recently, the aggregation of spheres (monomers) of BrC was observed in wildfire smoke from various parts of the world. Aggregation could alter the optical properties and direct radiative forcing of BrC, yet very little is known about this phenomenon. This study improves upon the spectral optical properties of BrC aggregates observed in past field studies. We are motivated by the framework presented by Saleh et al. (2020) for the optical classification of carbonaceous particles across the black-brown continuum. Here, we simplify Saleh et al.'s BrC categorization into two sub-classes: dark brown carbon (d-BrC) and weakly-absorbing brown carbon (w-BrC). We calculate and compare the mass absorption cross-section (MAC), mass scattering cross-section (MSC), single scattering albedo (SSA), and asymmetry parameter (g) of d-BrC and w-BrC and determine their absorption and scattering enhancements due to aggregation. Polydisperse diffusion-limited cluster-cluster aggregation (p-DLCA) simulations generated 90 aggregates of varying monomer diameters, and discrete dipole approximation (DDA) was used to calculate optical properties of these aggregates at relevant refractive indices, monomer diameters, and incident wavelengths. We find that optical properties of BrC aggregates are more sensitive to the monomer number and mean diameter than to polydispersity. d-BrC has almost twice the MAC of w-BrC at 350 nm, but w-BrC has MSC values almost double that of d-BrC. Both MAC and MSC for both types of aerosol decrease with wavelength. Aggregation enhances optical properties, with smaller size parameters and lower imaginary part of the complex refractive index of aggregates resulting in stronger absorption and scattering.

Airborne transmission via virus-laden aerosols is a dominant route for the transmission of respiratory diseases, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Direct, non-invasive screening of respiratory virus aerosols in patients has been a long-standing technical challenge. Here, we introduce a point-of-care testing platform that directly detects SARS-CoV-2 aerosols in as little as two exhaled breaths of patients and provides results in under 60 s. It integrates a hand-held breath aerosol collector and a llama-derived, SARS-CoV-2 spike-protein specific nanobody bound to an ultrasensitive micro-immunoelectrode biosensor, which detects the oxidation of tyrosine amino acids present in SARS-CoV-2 viral particles. Laboratory and clinical trial results were within 20% of those obtained using standard testing methods. Importantly, the electrochemical biosensor directly detects the virus itself, as opposed to a surrogate or signature of the virus, and is sensitive to as little as 10 viral particles in a sample. Our platform holds the potential to be adapted for multiplexed detection of different respiratory viruses. It provides a rapid and non-invasive alternative to conventional viral diagnostics.

Brown carbon light absorptivity is associated with organic aerosol volatility and elemental carbon concentrations.

Estimation of aerosol radiative forcing continues to suffer from large uncertainties, partially from a lack of observations of aerosol optical properties. Limited measurements of the atmospheric aerosol imaginary refractive index (iRI) have been made, especially in some of the world's most polluted regions. In this study, we measured aerosol optical and micro‐physical properties at a regional site, Rohtak, India, representative of polluted cities in the Indo‐Gangetic plains in northern India. The average PM 2.5 measured during the campaign was 163 μg/m 3 with a single‐scatter albedo of 0.7, indicating the presence of strongly absorbing aerosol components. Measurements of aerosol absorption, scattering, and particle number size distributions were used to estimate the effective refractive index using an established Mie inversion technique. The calculated iRI was spectrally invariant in the visible region with values ranging between 0.076 and 0.145. Brown carbon absorption, estimated using an existing Mie optimization method, ranged 34–88 Mm −1 , with strongly absorbing mass absorption cross‐sections (∼1.9 m 2 /g). Higher iRI were observed during periods with higher brown carbon absorption, which are likely directly emitted from combustion sources. Low volatility organic carbon fractions dominated during these periods, with likely persistence of atmospheric absorption. The iRI values are at the upper end of previously reported ranges of urban aerosol iRI. In a sensitivity analysis to measured parameters, the absorption had the dominant effect on estimated iRI. Measured single scatter albedos, were lower than those from climate model simulations over the region, demonstrating the need for intrinsic property measurements to evaluate and constrain climate models.

AbstractReal-time surveillance of airborne SARS-CoV-2 virus is a technological gap that has eluded the scientific community since the beginning of the COVID-19 pandemic. Offline air sampling techniques for SARS-CoV-2 detection suffer from longer turnaround times and require skilled labor. Here, we present a proof-of-concept pathogen Air Quality (pAQ) monitor for real-time (5 min time resolution) direct detection of SARS-CoV-2 aerosols. The system synergistically integrates a high flow (~1000 lpm) wet cyclone air sampler and a nanobody-based ultrasensitive micro-immunoelectrode biosensor. The wet cyclone showed comparable or better virus sampling performance than commercially available samplers. Laboratory experiments demonstrate a device sensitivity of 77–83% and a limit of detection of 7-35 viral RNA copies/m3 of air. Our pAQ monitor is suited for point-of-need surveillance of SARS-CoV-2 variants in indoor environments and can be adapted for multiplexed detection of other respiratory pathogens of interest. Widespread adoption of such technology could assist public health officials with implementing rapid disease control measures.

Abstract. Organic aerosol (OA) emissions from biomass burning have been the subject of intense research in recent years, involving a combination of field campaigns and laboratory studies. These efforts have aimed at improving our limited understanding of the diverse processes and pathways involved in the atmospheric processing and evolution of OA properties, culminating in their accurate parameterizations in climate and chemical transport models. To bring closure between laboratory and field studies, wildfire plumes in the western United States were sampled and characterized for their chemical and optical properties during the ground-based segment of the 2019 Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field campaign. Using a custom-developed multiwavelength integrated photoacoustic-nephelometer (MIPN) spectrometer in conjunction with a suite of instruments, including an oxidation flow reactor equipped to generate hydroxyl (OH∙) or nitrate (NO3∙) radicals to mimic daytime or nighttime oxidative aging processes, we investigated the effects of multiple equivalent days or nights of OH∙/NO3∙ exposure on the chemical composition and mass absorption cross-sections (MAC(λ)) at 488 and 561 nm of OA emitted from wildfires in Arizona and Oregon. We found that OH∙ exposure reduced the wavelength-dependent MAC(λ) by a factor of 0.72 ± 0.08, consistent with previous laboratory studies. On the other hand, NO3∙ exposure increased it by a factor of up to 1.69 ± 0.38. The MAC enhancement following NO3∙ exposure was correlated with an enhancement in CHO1N and CHOgt1N ion families measured with an aerosol mass spectrometer.

We establish the kinetics of ballistic-to-diffusive (BD) transition observed in two-dimensional random walk using directional statistics. Directional correlation is parameterized using the walker's turning angle distribution, which follows the commonly adopted wrapped Cauchy distribution (WCD) function. During the BD transition, the concentration factor (ρ) governing the WCD shape is observed to decrease from its initial value. We next analytically derive the relationship between effective ρ and time, which essentially quantifies the BD transition rate. The prediction of our kinetic expression agrees well with the empirical datasets obtained from correlated random walk simulation. We further connect our formulation with the conventionally used scaling relationship between the walker's mean-square displacement and time.

The presence of atmospheric brown carbon (BrC) has been the focus of many recent studies. These particles, predominantly emitted from smoldering biomass burning, absorb light in the near-ultraviolet and short visible wavelengths and offset the radiative cooling effects associated with organic aerosols. Particle density dictates their transport properties and is an important parameter in climate models and aerosol instrumentation algorithms, but our knowledge of this particle property is limited, especially as functions of combustion temperature and fuel type. We measured the effective density (ρeff) and optical properties of primary BrC aerosol emitted from smoldering combustion of Boreal peatlands. Energy transfer into the fuel was controlled by selectively altering the combustion ignition temperature, and we find that the particle ρeff ranged from 0.85 to 1.19 g cm-3 corresponding to ignition temperatures from 180 to 360 °C. BrC particles exhibited spherical morphology and a constant 3.0 mass-mobility exponent, indicating no internal microstructure or void spaces. Upon partial thermal volatilization, ρeff of the remaining particle mass was confined to a narrow range between 0.9 and 1.1 g cm-3. These findings lead us to conclude that primary BrC aerosols from biomass burning have homogeneous internal composition, and their ρeff is in fact their actual density.

Constraining the complex refractive indices, optical properties and size of brown carbon (BrC) aerosols is a vital endeavor for improving climate models and satellite retrieval algorithms. Smoldering wildfires are the largest source of primary BrC, and fuel parameters such as moisture content, source depth, geographic origin, and fuel packing density could influence the properties of the emitted aerosol. We measured in situ spectral (375-1047 nm) optical properties of BrC aerosols emitted from smoldering combustion of Boreal and Indonesian peatlands across a range of these fuel parameters. Inverse Lorenz-Mie algorithms used these optical measurements along with simultaneously measured particle size distributions to retrieve the aerosol complex refractive indices (m=n+ix). Our results show that the real part n is constrained between 1.5 and 1.7 with no obvious functionality in wavelength (lambda), moisture content, source depth, or geographic origin. With increasing X from 375 to 532 nm, k decreased from 0.014 to 0.003, with corresponding increase in single scattering albedo (SSA) from 0.93 to 0.99. The spectral variability of K follows the Kramers-Kronig dispersion relation for a damped harmonic oscillator. For lambda >= 532 nm, both K and SSA showed no spectral dependency. We discuss differences between this study and previous work. The imaginary part K was sensitive to changes in FPD, and we hypothesize mechanisms that might help explain this observation. (C) 2017 Elsevier Ltd. All rights reserved.

The complex refractive index m=n+ik of a particle is an intrinsic property which cannot be directly measured, it must be inferred from its extrinsic properties such as the scattering and absorption cross-sections. Bohren and Huffman called this approach describing the dragon from its tracks, since the inversion of Lorenz-Mie theory equations is intractable without the use of computers. This article describes PyMieScatt, an open-source module for Python that contains functionality for solving the inverse problem for complex m using extensive optical and physical properties as input, and calculating regions where valid solutions may exist within the error bounds of laboratory measurements. Additionally, the module has comprehensive capabilities for studying homogeneous and coated single spheres, as well as ensembles of homogeneous spheres with user-defined size distributions, making it a complete tool for studying the optical behavior of spherical particles.

Light-absorbing organic aerosols, optically defined as brown carbon (BrC), have been shown to strongly absorb short visible solar wavelengths and significantly impact Earth's radiative energy balance. There currently exists a knowledge gap regarding the potential impacts of atmospheric processing on the absorptivity of such particles generated from biomass burning. Climate models and satellite retrieval algorithms parametrize the optical properties of BrC aerosols emitted from biomass burning events as unchanging throughout their atmospheric lifecycle. Here, using contact-free optical probing techniques, we investigate the effects of multiple-day photochemical oxidation on the spectral (375-532 nm) optical properties of primary BrC aerosols emitted from smoldering combustion of boreal peatlands. We find the largest effects of oxidation in the near-UV wavelengths, with the 375 nm imaginary refractive index and absorption coefficients of BrC particles decreasing by similar to 36% and 46%, respectively, and an increase in their single scattering albedo from 0.85 to 0.90. Based on simultaneous chemical characterization of particles, we infer a transition from functionalization to fragmentation reactions with increasing photooxidation. Simple radiative forcing efficiency calculations show the effects of aging on atmospheric warming attributed to BrC aerosols, which could be significant over snow and other reflective surfaces.