Particulate Matter Emission Rates Research Articles

Spatiotemporal variations in emission and particulate matter concentration outside a concentrated layer feeding operation

A year-round monitoring of Particulate Matter (PM) less than 2.5 μm in aerodynamic diameter (PM2.5) and Total Suspended Particulate (TSP) from a conventional cage laying hen house was conducted to reveal their spatiotemporal variations of concentrations downwind of tunnel fans. The PM Emission Rate (ER) and the correlations between PM with meteorological parameters and ventilation rate were studied. Ten self-developed Particle Concentration Monitoring Units were distributed to multiple horizontal (2, 25, and 50 m) and vertical (2 and 5 m) locations for PM examination. Four PM2.5 concentration categories were classified: good (0–35 μg m−3), moderate (35–75 μg m−3), unhealthy (75–150 μg m−3), and very unhealthy (150–350 μg m−3). Annual and monthly percentages of PM2.5 and TSP sampling hours for each category were calculated. Significant temporal variations in ER were observed, with monthly average ER ranging from 0.05 to 0.86 mg d−1 hen−1 for PM2.5 and 1.40–8.90 mg d−1 hen−1 for TSP. Approximately 10–40% of PM2.5 sampling hours exceeded 75 μg m−3, while no TSP concentration was higher than the recommended threshold. The monthly percentage for the “very unhealthy” PM2.5 category was higher in winter, whereas the highest monthly percentage for the 800–1,200 μg m−3 TSP category was found in summer. Higher PM2.5 concentrations were prone to be observed at further sampling points, while higher TSP concentrations were mostly detected near the fans or on both sides of the sampling area. PM2.5 and TSP were positively correlated with ambient temperature and ventilation rate, while negatively correlated with relative humidity.

Development of brake activity measurement method for heavy-duty vehicles

ABSTRACT Tailpipe PM (particulate matter) emissions have been reduced due to decades of tightening regulations, however non-tailpipe PM emissions are not regulated and are expected to become a significant source of traffic-related PM emissions. Previous studies have focused on emission measurement from laboratory and track tests. Their findings suggest brake wear PM emission rates are dependent on brake activity. Therefore, it is important to characterize brake emissions by first understanding the real-world brake activity from many different vehicle vocations and driving conditions. The goal of the current study is to establish a test method and analysis for brake activity measurements of heavy-duty vehicles. In this study, brake fluid pressure and brake pad temperature were measured for a heavy-duty vehicle during chassis and on-road driving tests. The chassis tests consisted of the Central Business District (CBD) cycle representative of a repetitive stop-and-go driving pattern of a bus, and the Urban Dynamometer Driving Schedule (UDDS) cycle representative of urban driving conditions of heavy-duty vehicles. The on-road tests consisted of a local Riverside City route focused on urban roads at low vehicle speeds with frequent braking, while the second route from Riverside City to Victorville focused on highway driving and downhill braking. The brake pad temperature of the triplicate CBD cycle gradually increased linearly with a slope of 2.3°C/min and the temperature per kinetic energy lost during braking increased by 2.3 × 10−5°C/J for the CBD cycle. The UDDS cycles had the largest kinetic energy loss between 3.2 × 103 to 3.0 × 105 J in the histogram. The local Riverside city route brake temperature increased by 2.0°C/min. The kinetic energy loss for the on-road tests were one order of magnitude larger than that of the dynamometer tests due to brake events occurring under higher speeds. Implications: The non-tailpipe source contributions to traffic related particulate matter (PM) emissions have surpassed that of tailpipe emissions. The results of this work provide a measurement method to obtain brake activity information for a heavy-duty vehicle, which is critical estimating emission inventory accurately.

Seasonal vehicle emission rate of chemical compounds related to fuel type from on-road tunnel measurement

The vehicle emission rates (VER) of particulate matter (PM) and gaseous compounds were determined using analysis of the concentrations inside and outside the tunnel. The types of vehicles flowing during the observation period were classified into gasoline, diesel, and liquefied petroleum gas (LPG) vehicles, and factors affecting the calculated vehicle emissions were identified. This study presented PM2.5, OC, EC, NH3, CO, and NO at 11.21, 3.81, 2.82, 16.74, 563.61, and 257.50 mg/km, respectively. The PM2.5 and NO were higher in winter than in summer. The VER of chemical compounds, such as polycyclic aromatic hydrocarbons, benzene, toluene, ethylbenzene, and xylene, were presented. The study results present the characteristics of vehicle emission rate according to the fuel type during the holiday and non-holiday periods. It is expected that the results of this study can be used as data for international responses to reduce vehicle emissions in the future.

Ambient particulate matter monitoring system using SDS011 sensor utilizing machine learning approach and ambit of blockchain technology

In recent days, air contaminated with Particulate Matter Pollutants (PM2.5and PM10) plays a major role in affecting the quality of air and it has a strong effect on the environment. PM2.5and PM10 released in the air due to the huge increase of the manufacturing yards have led to significantly increased and severe health issues for all living beings around the globe. These Particulate Matters emitted from the industrial units can stay suspended in the air for a very prolonged time. Thus, it is essential to measure and monitor the ambient Particulate Matter emission rate near the industries. However, the existing fixed air quality monitoring stations are challenging, nevertheless, due to thehigh energy utilization, small-scale monitoring, and privacy concerns. To resolve this concern, a low-cost portable sensor NOVA SDS011 can be mounted near the industries to study the impact of the concentration level of the Particulate Matter Pollutants (PM2.5and PM10) released from those industries. The entire detected values are transmitted to a scalable IoT platform in the cloud, which allows the processing, analyzing, and exportation of data as.xls/.csv files. These exported data are pushed through various Machine Learning (ML) Classifier algorithms to classify the data into multiple classes based on its AQI (Air Quality Index) values. In this study, various ML classifier algorithms were used and analyzed in which Random Forest Classifier Algorithm had performed with utmost accuracy to classify the six different classes of AQI. This study also explains the limitation of privacy concerns in existing systems and the scope of using blockchain technology to overcome security concerns.

Particulate Matter Emission Factors for Dairy Facilities and Cattle Feedlots during Summertime in Texas.

Particulate matter (PM) emissions from dairies and feedlot sources require regular emission factor update. Likewise, development of simple measurement technique to accurately measure pollution concentration is warranted to limit the impact of air pollution and take necessary actions. During June of 2020, a dairy facility from central Texas and a feedlot from the Texas Panhandle region, titled as Dairy B and Feedlot C, respectively, were chosen for measurement of PM emissions in the state of Texas to represent dairy facilities and cattle feedlots PM emission rates. Four stations, each assigned with an EPA-approved Federal Reference Method (FRM) sampler, Texas A&M University (TAMU) designed sampler and handheld non-FRM AEROCET (MET One Instruments) sampler for collocation, were selected within each sampling locations. Drones were also utilized mounted with a handheld AEROCET sampler for simultaneously sampling at a certain height. PM2.5 emissions of Dairy B were all below 24-h PM2.5 standard of 35 μg m-3 as specified by National Ambient Air Quality Standards (NAAQS) even at the 98th percentile. The PM ratio between regulated PM10 to PM2.5 was determined to make an estimate of relative percentage of coarser particles to fine particles in both feedlot and the dairy representative animal facilities. The maximum mean emission factor determined using AERMOD for PM2.5 and PM10 was found to be 0.53 and 7.09 kg 1000-hd-1 d-1, respectively, for the dairy facility while 8.93 and 33.42 kg 1000-hd-1 d-1, respectively, for the feedlot. A conversion factor and correlation matrix were developed in this study to relate non-FRM sampler data from the handheld AERCET samplers with FRM samplers. Cheaper handheld samplers (AEROCETs) may play a potential role in quick and relatively instant measurement of PM emissions to initiate necessary preventive actions to control PM emission from dairy facility and feedlot sources.

Fuel use and emission rates reduction potential for light-duty gasoline vehicle eco-driving

Eco-driving offers potential to reduce fuel use and emission rates for light-duty gasoline vehicles (LDGVs). The objective is to quantify real-world route-level and segment-level fuel use and emission rates reduction potential for LDGV eco-driving. Three million seconds of real-world speed trajectory data were analyzed based on predominantly naturalistic driving of 160 drivers on eight mesoscale routes. The routes were further divided into 199 segments. A Vehicle Specific Power modal model was used to estimate trajectory-average fuel use and emission rates of CO2, CO, hydrocarbons, NOx, and particulate matter and to identify eco-driving trajectories. For route-level eco-driving, fuel use and emission rates reduction potential ranges from 6% to 40%, compared to average fuel use and emission rates estimated based on all trajectories. Eco-driving focused on fuel savings typically reduced air emissions and vice versa. Route-level eco-driving typically but not always concurrently reduces segment-level fuel use and emission rates. These co-benefits and tradeoffs can be used to guide LDGV eco-driving decisions.

Realistic operation of two residential cordwood-fired outdoor hydronic heater appliances—Part 2: Particle number and size

ABSTRACT The use of wood as a fuel for home heating is a concern from an environmental health and safety perspective as biomass combustion appliances emit high concentrations of particulate matter. Wood burning significantly contributes to wintertime particulate matter concentrations in many states in the northern United States. Of particular concern are outdoor wood-fired hydronic heaters. These devices are concerning as they tend to have very large combustion chambers and typical use patterns can result in long periods of low output, which result in an increased particulate matter emission rate relative to high heat output operating conditions. In this study, the performance of two hydronic heaters operating under different combustion conditions, including four different heat output categories approximately corresponding to categories I–IV denoted in Environmental Protection Agency Method 28 Outdoor Wood-fired Hydronic Heaters, and during start-up and reloading events were investigated. Measurements of flue gas particulate number concentration and size for particles with aerodynamic diameters between 0.006 and 10 µm were made using a dilution sampling system. The measured particle number concentration in the flue gas was between 0.71 and 420 million particles per cubic centimeter and was dependent on fuel loading and heat output. For each hydronic heater tested, the highest average particle concentration was found at the beginning of each test during the cold-start condition. Additionally, the majority of the particles had aerodynamic diameters less than 0.100 µm (particles of this size made up between 64% and 97% of all particles) and less than 1% of all particles had aerodynamic diameters greater than 1 µm for all phases. For particles in the accumulation mode, between 0.100 and 1 µm, the mean particle diameter was dependent on fuel loading and heat output. Implications: In this work, we provide information on the particle number concentration and particle size of emissions from outdoor cord- wood-fired hydronic heaters. Wood-fired hydronic heater data is sparsely available compared to wood stove data. Thus, additionaldata from this source help to inform the work of modelers and policy makers interested in hydronic heaters. The test method used in this work is also novel, as it is more inclusive of real-world use cases than the current certification method. Our data helps to validate the test method and allows for comparisons between real-world use case scenarios, and idealized test cases.

PARTICULATE MATTER EMISSION SOURCES AND THEIRCONTROL TECHNOLOGIES

Particulate matter (PM) is a complex form of air pollution that includes all types of solid and liquid droplets present in the atmosphere. Primary PM Emission rates, sizes, and composition are difficult to predict since they rely not only on the industry, but also on the fuel properties, infrastructure, and other aspects of the emission process. Normal (volcanic eruptions, forest fires, and weathering of parent material and sea salt) and anthropogenic (power plants, internal combustion engines, and the thermo-degradation process) sources of airborne particulate matter exist. In order to uphold human health conditions and a clean climate; it has become important to reduce PM concentrations. Given the negative effects of particulates, their regulation is becoming increasingly necessary. In this article, we look at all of the origins of particulate matter, their negative consequences, and the monitoring systems that are used to minimize PM emissions. However, manufacturing and plant operations will get the most attention because they are the backbone of a country’s economy as well as one of the largest causes of PM pollution and traffic regulation.

Intermodal comparison of tailpipe emission rates between transit buses and private vehicles for on-road passenger transport

Modal shift from private vehicles (PVs) to transit buses has the potential to reduce energy use and emissions from on-road passenger transport. Comparisons between these modes may be sensitive to key factors, such as vehicle size, fuel and powertrains, passenger load, and travel routes. The objectives are to evaluate the sensitivity of emission rates to route alignment, and compare emission rates between PVs and buses accounting for variability in key factors. Real-world bus speed trajectories were measured on actual bus routes for four origin-destination pairs (ODPs). To evaluate the sensitivity of emission rates to route alignment, hypothetical alternative bus routes were posited based on shortest distance, shortest travel time, or observed PV routes for each ODP. Trajectories and emission rates for PVs were quantified based on prior measurements of two routes per ODP using portable emission measurement systems. Trip-based tailpipe CO2, CO, total hydrocarbons (THC), NOx, and particulate matter (PM) emission rates were estimated for each ODP for gasoline and gasoline-hybrid PVs based on a Vehicle Specific Power modal model and for compressed natural gas, diesel, and diesel-hybrid buses based on the Transit Bus Emissions Model. Break-even passenger load (BEPL) was quantified to assess the minimum bus passenger load needed to achieve lower per passenger-trip emissions compared to PVs. Bus emission rates per bus-trip on actual bus routes are generally higher than those on hypothetical routes. As a bounding analysis, compared to single-occupancy PVs, fully occupied buses are estimated to have 82%–94% lower CO2, 99% lower to 308% higher CO, 99% lower to 145% higher THC, 67% lower to 62% higher NOx, and 94%–99% lower PM emission rates per passenger-trip depending on vehicle size, fuel and powertrain, passenger load, and route. BEPL varies depending on vehicle size, fuel and powertrain, route, and pollutant. The relative importance of key factors affecting intermodal comparisons differs by pollutants. The intermodal comparison is also affected by interactions among key factors, such as passenger load and route alignment, which reinforces the need for joint consideration of key factors.

The impact of oxygen content in the primary air supply on fuel burning rate and pollutant emissions in a forced-draft biomass stove

Forced-draft biomass stoves have been proposed as a substitute for traditional solid fuel stoves in rural areas. The change in the O2 content of the supply air via flue gas recirculation (FGR) significantly affects the combustion and pollutant emission performances of this household stove type, which has not been thoroughly investigated. In this study, O2 content control system was designed to accurately adjust the O2 content of the primary air supply for a typical household forced-draft biomass stove. Then, six conditions with an O2 content of 16%, 17%, 18%, 19%, 20%, and 21% were tested. The dynamic burning rate and emission rates of carbon monoxide (CO), nitrous oxides (NOx), and particulate matter with an aerodynamic diameter ≤ 2.5 μm (PM2.5) were measured for each O2 content. The results showed that the average burning rate of the six O2 contents ranged from 9.6 to 16.9 g/min. There was a positive linear relationship between the average burning rate and the O2 content in the primary air supply. The CO and PM2.5 emission amounts exhibited the concave and convex quadratic relationships with O2 content, respectively, while trends in NOx emission amount had a negative linear relationship with the O2 content. The real-time burning rate and pollutant emission rates exhibited different trends that influenced the amount of emissions. When FGR was utilized with a constant primary airflow rate, an O2 content of 19.7% may be optimal for household biomass stoves. This study provides basic information on air supply design of household biomass stoves, which is essential for future clean stove design and improvement.

Evaluating agricultural tractors emissions using remote monitoring and emission tests in Beijing, China

Agricultural machinery has increasingly become a significant source of air pollution in China, more in-depth research is needed regarding the emissions. Given the seasonal and regional characteristics with agricultural machinery usage, it is necessary to accurately estimate the emission intensity and spatio-temporal distribution. This study using Beijing as the case study area, evaluated agricultural tractors emissions using remote monitoring and emission tests during 2020. The results show that tractors in Beijing emitted 80 t of carbon monoxide (CO), 21 t of hydrocarbons (HC), 104 t of nitrogen oxides (NO x ), and 10 t of particulate matter (PM), totalling 215 t in 2020 and that the emissions were unevenly distributed with the districts and counties of Beijing. Shunyi District contributed the most (31.546%) whilst Haidian District contributed the least, less than 1% of the total. The spatio-temporal variation of NO x and PM emissions showed that the higher emissions occurred in Spring and Autumn because of agricultural activities. Using the annual utilisation hours determined by remote monitoring throughout the year, the emission factors for tractors obtained from emission tests and the estimated emission inventory could supplement the emission database from agricultural machinery in the different regions of China to assist environmental pollution research and policy making. • Remote monitoring system of agricultural tractors was established. • 71 agricultural tractors were monitored all the year round in Beijing. • Gaseous and PM emission rates of 14 tractors were measured using PEMS. • The spatio-temporal distribution analysis of emissions was established.

Influence of test cycle and fuel property on fuel consumption and exhaust emissions of a heavy-duty diesel engine

In this study, dynamometer engine tests were conducted to investigate the impact of test cycle and fuel type on fuel consumption and exhaust emissions of a heavy-duty diesel engine. This study presented novel approaches by utilizing comprehensive statistical analysis to assess these impacts on particulate matter (PM), PM-bound polycyclic aromatic hydrocarbons (PAHs), and multiple gaseous pollutants. Four types of fuels—two conventional diesels (X and Y) and each with 5% blend of biodiesel—were used under European Transient Cycle (ETC) and European Stationary Cycle (ESC). There were statistically significant higher fuel consumption and emission rates (by 9–73%) under ETC than ESC due to more occurrences of lower engine speeds and loads under ESC, and X fuels had higher emission rates of PM and carbon dioxides ([CO2]; 2.1–13%) but lower rates of hydrocarbons ([THC]; 44%) attributed to higher cetane number, sulfur contents and boiling points. Compared with conventional diesel, biodiesel blend had slightly lower emission rates of PM, CO, and NOX (1.7–6.6%) but higher fuel consumption (1%) and CO2 and THC emission rates (0.9–2.1%). The results of this study contributed to the limited datasets on the interactive effects of test cycle with fuel property on diesel vehicle exhaust emissions.

Impact of vehicular movement on road dust resuspension and spatiotemporal distribution of particulate matter during construction activities

This paper presents the characteristics of ambient particulate matter (PM), resuspendable road dust and PM mass deposition in the human respiratory tract during preconstruction and construction phases. PM Emission Rates (PMER) due to resuspension and spatiotemporal distribution were estimated and compared on construction roads (CR) and non-construction roads (NCR) for both phases. The construction phase monitoring results demonstrated that the silt load (SL) and PMER at CR (SL = 26–47 g/m2, PM10ER = 18.1–43.8 g/VKT, PM2.5ER = 4.3–10.6 g/VKT) were significantly high when compared to NCR (SL = 3.0–12.5 g/m2, PM10ER = 0.3–7.5 g/VKT, PM2.5ER = 0.1–1.8 g/VKT). Preconstruction phase results showed 15 to 20 times lesser values. Spatial and temporal variation studies showed that maximum PM concentrations (PM10 = 270.1, PM2.5 = 71.8, PM1 = 56.3 μg/m3) were found during night at construction roads due to the movement of heavy-duty vehicles carrying excavated earth overnight. Between 0 and 100 m length of road on either side of the construction sites, average PM10 concentrations were greater than 250 μg/m3. Similarly, for distance between 100 and 200 m, 200–400 m and 400–500 m, the PM10 values ranged between 200 and 250 μg/m3, 150–200 μg/m3 and 100–150 μg/m3 respectively. The current study results clearly indicated that resuspension of road dust due to movement of heavy duty trucks highly influence the PM concentrations in the surrounding environment of a construction site. The MPPD model results indicated that the total deposition fraction of PM10 in construction workers airway during the construction phase was 74–78%, followed by PM2.5(23–54%) and PM1(20–25%). Integration of sustainable practices, use of pollution control technologies and implementation of policies at a local scale are the way forward to mitigate the pollution from construction activities.

Clean Production of Biofuel from Waste Cooking Oil to Reduce Emissions, Fuel Cost, and Respiratory Disease Hospitalizations

Renewable energies are cleaner forms of energy, and their use, has intensified in recent decades. Thus, this work presents a proposal for reducing the emissions, fuel cost, and respiratory disease hospitalizations using environmental cost accounting principles to produce biodiesel production from waste frying oil. In our methodology, we conducted surveys, and collected waste cooking oil samples from local households and restaurants in São Paulo city, Brazil. Then, we produced biodiesel using these samples. Data on air pollutants were collected and correlated with the number of hospitalizations for respiratory diseases and their costs. Our results indicate that 330,000 respiratory disease hospitalizations were recorded in São Paulo city between 2009 and 2018, and the total cost for the Brazilian government reached US $117 million. Improving the city air quality by switching from fossil fuels to biodiesel could reduce the annual number of hospitalizations to 9880 and cost US $3.518 million, because the amount of pollutants emitted from burning fossil fuels was positively correlated with the number of respiratory disease hospitalizations and their costs. Moreover, the emission rates of particulate matter with particles less than 10 and 2.5 µm in diameter exceeded the World Health Organization limits throughout the study period. Using the survey data, we estimated that the average monthly quantity of waste cooking oil was 9794.6 m3, which could generate 9191.2 m3 of biodiesel and produce 239,713 t CO2 of carbon credits. Environmental cost accounting revealed that it would be possible to achieve an annual profit of approximately US $300 million from the sale of excess biodiesel, carbon credits, and glycerine, and fuel acquisition savings which could improve the image of São Paulo city and quality of life of its residents. Thus, we present this as a way to reduce cost and hospitalizations, and increase the number of available hospital beds for other diseases, such as COVID-19.

Effect of silt loading on particle concentration in the atmosphere from resuspended road dust through particulate matter dispersion modeling

Particulate matter (PM) is one of the most dangerous pollutants in urban environments, and studies have linked it to many adverse health effects. Resuspended road dust makes up a large portion of PM. Therefore, monitoring and estimating the contribution of resuspended dust to PM is vital. In this study, emission rates of fine and coarse PM from different roads of Vellore City in Tamil Nadu, India, is estimated using silt loading protocol adapted by U.S. EPA. Silt load samples are collected from 8 different roads in the study region. The emission rates estimated using silt loading and fleet weight are then used to model the dispersion of PM throughout the study region using AERMOD. Hotspots of high PM concentration are identified at various locations frequented by commuters in the study region. Areas around educational institutions and hospitals in the region show highly elevated levels of PM. This study illustrates that resuspension of road dust can contribute significantly to PM in urban centers. Fine PM concentration exceeding 500 μg/m3 raises concerns about the health of commuters in the study region. This justifies the need for more regulations aiming at reducing the contribution of PM from resuspended road dust to overall urban PM.

Particulate Matter Concentrations and Emission Rates at Two Retrofitted Manure-Belt Layer Houses

HighlightsAnnual average PM10 (daily) and PM2.5 (hourly) concentrations of 509 ±203 and 43.3 ±36.1 µg m-3, respectively.Significant seasonal and diurnal variations in PM concentrations; higher in colder seasons and during daytime.Annual average PM10 (daily) and PM2.5 (hourly) emission rates of 17.9 ±9.6 and 1.41 ±1.53 mg d-1 hen-1, respectively.Significant diurnal variations in PM emission rates but inconsistent seasonal variations; higher during daytime.Abstract. Particulate matter (PM) concentration and emission rate information is needed to assess air quality concerns and environmental impacts related to egg production. Many egg layer buildings were retrofitted from high-rise to manure-belt houses over the past few decades. However, no studies have reported these PM data for the retrofitted layer houses. In this study, concentrations and emission rates of PM10 and PM2.5 were continuously monitored for 12 months at two commercial retrofitted manure-belt layer houses in the Midwestern U.S. The average daily mean PM10 and average hourly mean PM2.5 concentrations in the houses were 509 ±203 µg m-3 and 43.3 ±36.1 µg m-3, respectively. Significant seasonal and diurnal variations in PM concentrations were observed, with higher concentrations in colder seasons and during daytime. The average daily mean PM10 and average hourly mean PM2.5 emission rates from the two houses were 17.9 ±9.6 mg d-1 hen-1 and 1.41 ±1.53 mg d-1 hen-1, respectively. There were no consistent patterns in seasonal variations. Significant diurnal variations were also observed for PM emission rates, with higher PM emission rates observed during daytime. The PM concentrations were significantly correlated with ambient temperature, ventilation rate, bird age, egg production, daily mortality, and feed conversion ratio, while PM emission rates were significantly correlated with ambient temperature, bird age, bird mass, egg mass, and ventilation rate. Keywords: Diurnal variation, Dust, Egg production, PM, Poultry, Seasonal variation.

A Semi-portable Enclosure to Measure Emission Rates from Small Fugitive Sources: Leaf Blower Dust Example

Small fugitive particulate matter (PM) sources may make significant impacts to urban air pollution. The PM emission rates for most of these have not been measured due to the difficulty involved. The EPA addresses some fugitive emission rates in their AP-42 document and these generally were developed from upwind-downwind sampling. A semi-portable enclosure was designed and evaluated for determining PM emission rates from landscape cleaning operations such as leaf blowing, raking, and sweeping. The method consisted of an enclosure to confine PM emissions while performing these activities on actual in-use surfaces while making measurements using real-time PM analyzers. In this enclosure the cleaning equipment could be operated in the same way as normally done. To evaluate the enclosure’s mixing and exchange rates, propene gas was released and used as a tracer. PM emission rates were then calculated from measured concentrations, the dimensions of the enclosure, and area over which the cleaning operation was performed. To evaluate this method, cleaning operations were performed after the pavement floor was vacuumed and then spiked with surrogate debris. To make actual emission rate measurements, the enclosure was placed over landscape with debris from normal grounds maintenance activities. Emission rates were determined as a function of surface type and cleaning tool used. The method would be appropriate for many types of small fugitive dust sources.

Characteristics of Particulate Matter Emissions from Swine and Poultry Production Houses in the United States

HighlightsParticulate matter (PM) data were analyzed to identify PM emission characteristics among different animal types.The PM concentrations were higher in broiler chicken and swine farrowing houses and were higher in winter.The PM emissions were also higher in broiler chicken houses and swine farrowing rooms.The PM in the layer chicken house in Indiana had narrower distributions with a greater percentage of smaller particles.Abstract. Understanding the characteristics of particulate matter (PM) emissions from animal feeding operations (AFOs) is essential to address the associated health and environmental impacts and to develop control strategies to mitigate such impacts. This article reports a study of PM concentrations and emission characteristics from 26 poultry and swine production houses to investigate the similarities and differences in PM emission characteristics, e.g., concentrations, emission rates, and particle size distribution (PSD), among different animal and housing types. Concentration and emission data for PM2.5, PM10, and total suspended particulates (TSP) collected by the National Air Emission Monitoring Study (NAEMS) were used to compare the differences among different production practices and animal types. The PSDs of the PM were examined based on the PM2.5/PM10 and PM10/TSP emission rate ratios. It was discovered that the concentrations of PM varied among animal types. For poultry, the concentrations of PM were higher in broiler houses than in other poultry houses. For swine, the average concentrations of PM were higher in farrowing rooms than in swine barns. Moreover, the PM concentrations in poultry and swine houses exhibited significant seasonal trends, with higher concentrations in winter and lower concentrations in summer, which were in a reverse relationship with ventilation rates. The PM emissions also varied among animal types. For poultry, the PM emissions were significantly higher for poultry production houses in California. For swine, the PM emissions were significantly higher for farrowing rooms than other swine houses. The PSD of PM varied among animal types, with mass median diameters (MMD) in the ranges of 6.51 to 13.62 µm for poultry houses and 7.94 to 17.19 µm for swine houses. The geometric standard deviations (GSD) were in the ranges of 1.66 to 2.71 and 1.65 to 2.9 for poultry and swine PM, respectively. The PM in the layer house in Indiana had a narrower distribution (smaller GSD) with a greater percentage of smaller particles than the other poultry houses, while the PM in the broiler house in California had a broader distribution (larger GSD) than the other poultry houses. For swine, the PM in the sow barn in North Carolina had a narrower distribution (smaller GSD) than the other swine houses, while the PM in the farrowing houses in Oklahoma had a broader distribution (larger GSD) than the other swine houses. The knowledge gained from this research may provide insights for addressing the PM emissions from various animal production systems. Keywords: Concentration and emission, Particulate matter, PM2.5, PM10, Poultry, swine, PSD, TSP.

Advances in the estimation of high Spatio-temporal resolution pan-African top-down biomass burning emissions made using geostationary fire radiative power (FRP) and MAIAC aerosol optical depth (AOD) data

We provide major updates to the ‘top down’ Fire Radiative Energy Emissions (FREM) approach to biomass burning emissions calculations, bypassing the estimation of fuel consumption that is a major source of uncertainty in widely used ‘bottom up’ approaches. The FREM approach links satellite observations of fire radiative power (FRP) to emission rates of total particulate matter (TPM) via spatially varying smoke emissions coefficients (g.MJ−1) – each derived from matchups of FRP and smoke plume aerosol optical depth (AOD). In the original FREMv1 approach, FRP data came from the geostationary Meteosat satellite and AOD data from the 10 km spatial resolution MODIS MOD04 aerosol product. However, the latter often performs quite poorly close to biomass burning sources due to its large 10 km pixels, bias at high MODIS view zenith angles, and saturation and/or removal of areas of high AOD - limitations introducing bias and uncertainty into the final FREM-derived smoke emissions estimates. We address each of these issues through a series of significant methodological and input data improvements, including exploitation of the 1 km MODIS MAIAC AOD product that performs far better close to fire sources. We use our FREMv2 methodology to generate a new pan-African fire emissions inventory for TPM and the carbonaceous gases CO2, CO and CH4, and our annual mean TPM emissions are within 11% of those of the MODIS-based FEER top-down approach, but significantly higher than those of GFASv1.2 and GFEDv4.1s (by 114% and 69% respectively) - agreeing with independent assessments that aerosol emissions of GFASv1.2 require upscaling by a factor of 2 to 3.4 to deliver matching magnitudes between modelled and observed AODs. From our carbonaceous emissions totals we map dry matter consumed (DMC) across Africa, and dividing this by the FireCCISFD11 20 m burned area product we provide one of the first data-driven pan-African maps of fuel consumption per unit area (kg.m−2) which in many areas is higher than in GFEDv4.1s. Our estimates represent the highest spatio-temporal resolution biomass burning emissions data yet available over Africa, and significantly advance the aim of a pan-tropical and mid-latitude inventory based on FRP from the global geostationary satellite network (Meteosat, Meteosat IOD, GOES and Himawari).

Particulate matter in mechanically ventilated high school classrooms

This two year study is based on measurements of particulate matter (PM) in seven high schools in Central Texas, each with mechanical ventilation. Measurements were completed in 39 classrooms, including five portable (temporary) classrooms. Each classroom was sampled between one and four four-day sampling events. The average PM2.5 and PM10 concentrations in the classrooms were lower than standards established by WHO and ASHRAE. The I/O ratios for PM2.5 and PM10 in this study were much lower than those reported in many other related studies, which may be due to use of filtration systems in all schools that participated in this study. Indoor PM1 and PM2.5 concentrations correlated with outdoor concentrations. However, indoor and outdoor PM10 did not correlate, indicating that the primary source of indoor PM10 is resuspension from indoor surfaces, specifically flooring. There was no significant difference in size-resolved particulate matter between regular (permanent)and portable classrooms during school hours. Flooring type had a significant effect on indoor PM. For PM1, PM2.5 and PM10, carpet flooring was associated with 1.4, 1.8, and 3.8 times higher indoor concentrations relative to classrooms containing vinyl composition tile (VCT) flooring. The average PM emission rates per student in regular classrooms with VCT, regular classrooms with carpet, and portable classrooms with carpet were 0.10 mg/h, 0.11 mg/h and 0.12 mg/h for PM2.5 and 0.51 mg/h, 1.84 mg/h and 1.59 mg/h for PM10. The reported PM emission rates described herein can be used to better estimate indoor PM concentrations in classrooms.