Dynamic interplay between social distancing duration and intensity in reducing COVID-19 US hospitalizations: A "law of diminishing returns".

We uncover and highlight the importance of social distancing duration and intensity in lowering hospitalization demand-to-supply during the coronavirus disease 2019 (COVID-19) epidemic in the USA. We have developed an epidemic progression model involving the susceptible-exposed-infected-recovered dynamics, the age-stratified disease transmissibility, and the possible large-scale undocumented (i.e., asymptomatic and/or untested) transmission of COVID-19 taking place in the USA. Our analysis utilizes COVID-19 observational data in the USA between March 19 and 28, corresponding to the early stage of the epidemic when the impacts of social distancing on disease progression were yet to manifest. Calibrating our model using epidemiological data from this time period enabled us to unbiasedly address the question "How long and with what intensity does the USA need to implement social distancing intervention during the COVID-19 pandemic?" For a short (i.e., up to two weeks) duration, we find a near-linear decrease in hospital beds demand with increasing intensity (φ) of social distancing. For a duration longer than two weeks, our findings highlight the diminishing marginal benefit of social distancing, characterized by a linear decrease in medical demands against an exponentially increasing social distancing duration. Long-term implementation of strict social distancing with φ>50% could lead to the emergence of a second wave of infections due to a large residual susceptible population which highlights the need for contact tracing and isolation before re-opening of the economy. Finally, we investigate the scenario of intermittent social distancing and find an optimal social-to-no-distancing duration ratio of 5:1 corresponding to a sustainable reduction in medical demands.

Portable device to determine particle asymmetry parameter.

Accurate characterization of the asymmetry parameter g is of crucial importance for radiative transfer calculations. Here, we present a portable light scattering (PLS) device designed for in situ, real-time, and contact-free measurements of the particle phase function. The integration time is 20ms for each measurement. Using this device, we measured g values for laboratory-generated brown (organic) carbon aerosol and soot, and commercially available Arizona Road Dust. For the three types of aerosol, the g values were 0.664 ± 0.002, 0.506 ± 0.004, and 0.701 ± 0.020, respectively. The smallest angle our PLS device approaches is 0.7°, which is an order of magnitude smaller than that of commercial nephelometers, thus facilitating more accurate determination of g.

Scaling Laws for Light Absorption Enhancement Due to Nonrefractory Coating of Atmospheric Black Carbon Aerosol.

Black carbon (BC) aerosol, the strongest absorber of visible solar radiation in the atmosphere, contributes to a large uncertainty in direct radiative forcing estimates. A primary reason for this uncertainty is inaccurate parametrizations of the BC mass absorption cross section (MAC_{BC}) and its enhancement factor (E_{MAC_{BC}})-resulting from internal mixing with nonrefractory and nonlight absorbing materials-in climate models. Here, applying scaling theory to numerically exact electromagnetic calculations of simulated BC particles and observational data on BC light absorption, we show that MAC_{BC} and E_{MAC_{BC}} evolve with increasing internal mixing ratios in simple power-law exponents of 1/3. Remarkably, MAC_{BC} remains inversely proportional to the wavelength of light at any mixing ratio. When mixing states are represented using mass-equivalent core-shell spheres, as is done in current climate models, it results in significant underprediction of MAC_{BC}. We elucidate the responsible mechanism based on shielding of photons by a sphere's skin depth and establish a correction factor that scales with a ¾ power-law exponent.

Density and Homogeneous Internal Composition of Primary Brown Carbon Aerosol.

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.

Temporal and spatial variations of PM2.5 organic and elemental carbon in Central India.

This study describes spatiotemporal patterns from October 2015 to September 2016 for PM2.5 mass and carbon measurements in rural (Kosmarra), urban (Raipur), and industrial (Bhilai) environments, in Chhattisgarh, Central India. Twenty-four-hour samples were acquired once every other week at the rural and industrial sites. Twelve-hour daytime and nighttime samples were acquired either a once a week or once every other week at the urban site. Each site was equipped with two portable, battery-powered, miniVol air samplers with PM2.5 inlets. Annual average PM2.5 mass concentrations were 71.8 ± 27 µg m-3 at the rural site, 133 ± 51 µg m-3 at the urban site, and 244.5 ± 63.3 µg m-3 at the industrial site, ~ 2-6 times higher than the Indian Annual National Ambient Air Quality Standard of 40 µg m-3. Average monthly nighttime PM2.5 and carbon concentrations at the urban site were consistently higher than those of daytime from November 2015 to April 2016, when temperatures were low. Annual average total carbon (TC = OC + EC) at the urban (46.8 ± 23.8 µg m-3) and industrial (98.0 ± 17.2 µg m-3) sites also exceeded the Indian PM2.5 NAAQS. TC accounted for 30-40% of PM2.5 mass. Annual average OC ranged from 17.8 ± 6.1 µg m-3 at the rural site to 64 ± 9.4 µg m-3 at the industrial site, with EC ranging from 4.51 ± 2.2 to 34.01 ± 7.8 µg m-3. The average OC/EC ratio at the industrial site (1.88) was 18% lower than that at the urban site and 52% lower than that at the rural site. OC was attributed to 43.0% of secondary organic carbon (SOC) at the rural site, twice that estimated for the urban and industrial sites. Mortality burden estimates for PM2.5 EC are 4416 and 6196 excess deaths at the urban and industrial sites, respectively, during 2015-2016.

Spatio-temporal measurement of indoor particulate matter concentrations using a wireless network of low-cost sensors in households using solid fuels.

Many households use solid fuels for cooking and heating purposes. There is currently a knowledge gap in our understanding of the variations in indoor air quality throughout the household as most of the studies focus on the areas in the close proximity of the cookstove. A low-cost wireless particulate matter (PM) sensor network was developed and deployed in households in Raipur, India to establish the spatio-temporal variation of PM concentrations. The data from multiple sensors were acquired in real-time with a wireless system. Data collected from the sensors agreed well (R2 =0.713) with the reference data collected from a commercially available instrument. Low spatial variability was observed within the kitchen due to its small size and poor ventilation - a common feature of most rural Indian kitchens. Due to insufficient ventilation from open doors and windows, high PM concentrations similar to those found in the kitchen were also found in the adjoining rooms. The same household showed significantly different post-extinguished cookstove PM concentration decay rates (0.26mg/m3-min and 0.87mg/m3-min) on different days, owing to varying natural air exchange rates (7.68m3/min and 37.40m3/min).

Fractal morphology of black carbon aerosol enhances absorption in the thermal infrared wavelengths.

In this Letter, we numerically calculate the mass absorption cross sections (MACs) of black carbon fractal aggregates in the thermal infrared solar spectrum. Compared to equivalent-size spheres, the MAC values of aggregates show a percent enhancement of ≈150 and 400 at small and large length scales, respectively. The absorption properties of aggregates with size parameters >1 surprisingly continued to remain in the Rayleigh optics regime. We explain this phenomenon using the Maxwell-Garnett effective medium theory and the concept of phase shift parameter.

Scattering directionality parameters of fractal black carbon aerosols and comparison with the Henyey-Greenstein approximation.

Current radiation transfer schemes employ the Henyey-Greenstein (HG) phase function to connect three single parameter representations of aerosol scattering directionality-the hemispherical upscatter fraction (ß), the backscatter fraction (b), and the asymmetry parameter (g). The HG phase function does not account for particle morphology, which could lead to significant errors. In this Letter, we compute these single parameters for fractal black carbon (BC) aerosols using the numerically exact superposition T-matrix method. The variations in ß, g, and b as a function of aerosol morphology are examined. Corrected empirical relationships connecting these parameters are proposed. We find that the HG phase function could introduce up to a 35% error in ß and g estimates. Interestingly, these errors are suppressed by the large mass absorption cross-sections of BC aerosols in radiative transfer calculations and contribute to ≤8% error in direct forcing efficiencies.

Empirical relationships between optical properties and equivalent diameters of fractal soot aggregates at 550 nm wavelength.

Soot aggregates (SAs)-fractal clusters of small, spherical carbonaceous monomers-modulate the incoming visible solar radiation and contribute significantly to climate forcing. Experimentalists and climate modelers typically assume a spherical morphology for SAs when computing their optical properties, causing significant errors. Here, we calculate the optical properties of freshly-generated (fractal dimension Df = 1.8) and aged (Df = 2.6) SAs at 550 nm wavelength using the numerically-exact superposition T-Matrix method. These properties were expressed as functions of equivalent aerosol diameters as measured by contemporary aerosol instruments. This work improves upon previous efforts wherein SA optical properties were computed as a function of monomer number, rendering them unusable in practical applications. Future research will address the sensitivity of variation in refractive index, fractal prefactor, and monomer overlap of SAs on the reported empirical relationships.

Characterizing elemental, equivalent black, and refractory black carbon aerosol particles: a review of techniques, their limitations and uncertainties.

Elemental-, equivalent black- and refractory black-carbon are terms that have been defined in order to dissect the more general term, black carbon, into its component parts related to its specific chemical and optical properties and its impact on climate and health. Recent publications have attempted to clarify the meaning of these terms with respect to their environmental impact, particularly on climate. Here, we focus on the measurement aspects, reviewing the most commonly implemented techniques for the direct and indirect derivation of black carbon properties, their strengths, limitations, and uncertainties, and provide a non-exhaustive bibliography where the reader can find more detailed information. This review paper is designed as a guide for those wishing to learn about the current state of black carbon measurement instrumentation, how calibration is carried out, when one instrument may have the advantage over another, and where new techniques are needed to fill important knowledge gaps.

A High-Avidity, Thermostable, and Low-Cost Synthetic Capture for Ultrasensitive Detection and Quantification of Viral Antigens and Aerosols.

Lateral flow assays (LFAs) are currently the most popular point-of-care diagnostics, rapidly transforming disease diagnosis from expensive doctor checkups and laboratory-based tests to potential on-the-shelf commodities. Yet, their sensitive element, a monoclonal antibody, is expensive to formulate, and their long-term storage depends on refrigeration technology that cannot be met in resource-limited areas. In this work, LCB1 affibodies (antibody mimetic miniproteins) were conjugated to bovine serum albumin (BSA) to afford a high-avidity synthetic capture (LCB1-BSA) capable of detecting the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein and virus like particles (VLPs). Substituting the monoclonal antibody 2B04 for LCB1-BSA (stable up to 60 °C) significantly improved the thermal stability, shelf life, and affordability of plasmonic-fluor-based LFAs (p-LFAs). Furthermore, this substitution significantly improved the sensitivity of p-LFAs toward the spike protein and VLPs with precise quantitative ability over 2 and 3 orders of magnitude, respectively. LCB1-BSA sensors could detect VLPs at 100-fold lower concentrations, and this improvement, combined with their robust nature, enabled us to develop an aerosol sampling technology to detect aerosolized viral particles. Synthetic captures like LCB1-BSA can increase the ultrasensitivity, availability, sustainability, and long-term accuracy of LFAs while also decreasing their manufacturing costs.

Shortwave absorption by wildfire smoke dominated by dark 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.

Real-time environmental surveillance of SARS-CoV-2 aerosols.

Real-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.

Rapid Direct Detection of SARS-CoV-2 Aerosols in Exhaled Breath at the Point of Care.

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.

Fractality of tics as a quantitative assessment tool for Tourette syndrome.

Tics manifest as brief, purposeless and unintentional movements or noises that, for many individuals, can be suppressed temporarily with effort. Previous work has hypothesized that the chaotic temporal nature of tics could possess an inherent fractality, that is, have neighbour-to-neighbour correlation at all levels of timescale. However, demonstrating this phenomenon has eluded researchers for more than two decades, primarily because of the challenges associated with estimating the scale-invariant, power law exponent-called the fractal dimension Df-from fractional Brownian noise. Here, we confirm this hypothesis and establish the fractality of tics by examining two tic time series datasets collected 6-12 months apart in children with tics, using random walk models and directional statistics. We find that Df is correlated with tic severity as measured by the YGTTS total tic score, and that Df is a sensitive parameter in examining the effect of several tic suppression conditions on the tic time series. Our findings pave the way for using the fractal nature of tics as a robust quantitative tool for estimating tic severity and treatment effectiveness, as well as a possible marker for differentiating typical from functional tics.

Ambient PM2.5 exposure and rapid spread of COVID-19 in the United States.

It has been posited that populations being exposed to long-term air pollution are more susceptible to COVID-19. Evidence is emerging that long-term exposure to ambient PM2.5 (particulate matter with aerodynamic diameter 2.5 µm or less) associates with higher COVID-19 mortality rates, but whether it also associates with the speed at which the disease is capable of spreading in a population is unknown. Here, we establish the association between long-term exposure to ambient PM2.5 in the United States (US) and COVID-19 basic reproduction ratio R0- a dimensionless epidemic measure of the rapidity of disease spread through a population. We inferred state-level R0 values using a state-of-the-art susceptible, exposed, infected, and recovered (SEIR) model initialized with COVID-19 epidemiological data corresponding to the period March 2-April 30. This period was characterized by a rapid surge in COVID-19 cases across the US states, implementation of strict social distancing measures, and a significant drop in outdoor air pollution. We find that an increase of 1 µg/m3 in PM2.5 levels below current national ambient air quality standards associates with an increase of 0.25 in R0 (95% CI: 0.048-0.447). A 10% increase in secondary inorganic composition, sulfate-nitrate-ammonium, in PM2.5 associates with ≈10% increase in R0 by 0.22 (95% CI: 0.083-0.352), and presence of black carbon (soot) in the ambient environment moderates this relationship. We considered several potential confounding factors in our analysis, including gaseous air pollutants and socio-economical and meteorological conditions. Our results underscore two policy implications - first, regulatory standards need to be better guided by exploring the concentration-response relationships near the lower end of the PM2.5 air quality distribution; and second, pollution regulations need to be continually enforced for combustion emissions that largely determine secondary inorganic aerosol formation.

Kinetics of sol-to-gel transition in irreversible particulate systems.

A comprehensive theory encompassing the kinetics of the sol-to-gel transition is yet to be formulated due to break-down of the mean-field Smoluchowski Equation. Using high temporal-resolution Monte Carlo simulation of irreversible aggregation systems, we show that this transition has three distinct regimes with kinetic exponent z∈1,2 corresponding to aggregation of sol clusters proceeding to the ideal gel point (IGP); z∈2,5.7 for gelation of sol clusters beyond IGP; and z∈2,3.5 for a hitherto unidentified regime involving aggregation of gels when monomer-dense. We further establish universal power-law scaling relationships that connect the kinetics of these three regimes. Improved parameterizations are performed on the characteristic timescale parameters that define each regime.

Household solid fuel burning emission characterization and activity levels in India.

Emission factors (EFs) of PM2.5, carbon fractions, major ionic (K+, Ca2+, NH4+, SO42-, NO3- and Cl-) and elemental (Al, Cr, Cu and Fe) species from combustion of commonly used household solid fuel were determined in 10 different states in India during cooking practices. The study involved sampling during actual household cooking involving use of a variety of fuels including coal balls (CB), fuel wood (FW), dung cakes (DC), crop residues (CR), mixed fuels (MF: dung cakes + fuel woods). Species-wise highest EFs (g·kg-1) were: 34.16 ±â€¯10.1 for PM2.5 (CB), 14.18 ±â€¯5.8 for OC (CB), 2.33 ±â€¯1.4 for EC (DC), 1.03 ±â€¯0.2 for K+ (CR), 2.21 ±â€¯0.6 NH4+ (DC), 0.61 ±â€¯0.2 for NO3- (CB), 0.59 ±â€¯0.1 for SO42- (CB), 0.69 ±â€¯0.1 for Cl- (CR) among the fuels. Higher OC EFs for CB could be attributed to higher moisture content (>13%) in coal-powder that is used to handmade coal balls. It is observed that, in general, OC3 and EC1 were the dominant thermally evolved carbon mass fractions. The study averaged MCE values were in the range 0.93-0.98, which could be attributed to higher variability in flaming and smoldering episodes during the combustion of selected fuels. Sum of ionic EFs for emissions from DC, CR and MF were found to be higher than those observed for FW and CB. The K+/EC and Cl-/EC (~1) ratios could be better indicators of CR fuels to differentiate it from FW, whereas NH4+/EC (~1) is suitable to indicate DC. Average annual emission estimates of PM2.5 (2.00 ±â€¯0.53 Tg·yr-1), OC (0.86 ±â€¯0.23 Tg·yr-1) and EC (0.11 ±â€¯0.02 Tg·yr-1) for tested fuels are evaluated to be contributing 27, 15 and 4% of total PM2.5, OC and EC, respectively, toward annual emission budget from different anthropogenic activities in India.

Extensive Soot Compaction by Cloud Processing from Laboratory and Field Observations.

Soot particles form during combustion of carbonaceous materials and impact climate and air quality. When freshly emitted, they are typically fractal-like aggregates. After atmospheric aging, they can act as cloud condensation nuclei, and water condensation or evaporation restructure them to more compact aggregates, affecting their optical, aerodynamic, and surface properties. Here we survey the morphology of ambient soot particles from various locations and different environmental and aging conditions. We used electron microscopy and show extensive soot compaction after cloud processing. We further performed laboratory experiments to simulate atmospheric cloud processing under controlled conditions. We find that soot particles sampled after evaporating the cloud droplets, are significantly more compact than freshly emitted and interstitial soot, confirming that cloud processing, not just exposure to high humidity, compacts soot. Our findings have implications for how the radiative, surface, and aerodynamic properties, and the fate of soot particles are represented in numerical models.

Establishing the kinetics of ballistic-to-diffusive transition using directional statistics.

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.