Yu Wang

Biomass burning (BB) aerosol particles can significantly impact climate, air quality, and human health. While studies have advanced our understanding of the impact-related properties of the particles, detailed investigations of both driving factors and mechanisms under controlled conditions of dark aging or photooxidation remain limited, especially across fuel types and burn phases. Here, we describe a BB aerosol facility at the University of Manchester that couples an Esse 175F commercial wood-burning stove, housed in a mobile unit outside the main laboratory and supplied with ambient combustion air, to the 18 m³ Manchester Aerosol Chamber. The facility enables batch-mode experiments under controlled dilution, temperature, relative humidity, oxidant exposure, target initial particle mass loading, and dark or illuminated aging conditions. Its batch configuration uses moderate, rather than high, continuous dilution, thereby limiting rapid dilution-induced perturbations while maintaining stable conditions and particle concentrations suitable for filter-based analyses, long-term parallel continuous measurements, and slow-response instruments that are difficult to deploy in short-residence-time flow systems. Measurements include particle size distribution, chemical composition, black carbon, volatility, hygroscopic growth, cloud condensation nuclei activity, optical properties, ice-nucleating ability, and gases. This facility, therefore, provides a repeatable, flexible system and platform for mechanistic and multiparameter investigation of fresh and aged BB aerosol. Experiments conducted in April and September 2022 demonstrated those capabilities across fuel types and combustion phases. They also characterized the facility, showing low background PM levels (<1 µg/m³), with primary gases and PM concentrations typically stabilizing within no more than 10–30 min after smoke injection.

Biomass burning (BB) aerosol particles can significantly impact climate, air quality, and human health. While studies have advanced our understanding of the impact-related properties of the particles, detailed investigations of both driving factors and mechanisms under controlled conditions of dark aging or photooxidation remain limited, especially across fuel types and burn phases. Here, we describe a BB aerosol facility at the University of Manchester that couples an Esse 175F commercial wood-burning stove, housed in a mobile unit outside the main laboratory and supplied with ambient combustion air, to the 18 m³ Manchester Aerosol Chamber. The facility enables batch-mode experiments under controlled dilution, temperature, relative humidity, oxidant exposure, target initial particle mass loading, and dark or illuminated aging conditions. Its batch configuration uses moderate, rather than high, continuous dilution, thereby limiting rapid dilution-induced perturbations while maintaining stable conditions and particle concentrations suitable for filter-based analyses, long-term parallel continuous measurements, and slow-response instruments that are difficult to deploy in short-residence-time flow systems. Measurements include particle size distribution, chemical composition, black carbon, volatility, hygroscopic growth, cloud condensation nuclei activity, optical properties, ice-nucleating ability, and gases. This facility, therefore, provides a repeatable, flexible system and platform for mechanistic and multiparameter investigation of fresh and aged BB aerosol. Experiments conducted in April and September 2022 demonstrated those capabilities across fuel types and combustion phases. They also characterized the facility, showing low background PM levels (<1 µg/m³), with primary gases and PM concentrations typically stabilizing within no more than 10–30 min after smoke injection.

The hygroscopicity parameter κ is important for assessing biomass-burning aerosol impacts on visibility, direct radiative forcing, and cloud-related indirect effects. Discrepancies among κ values derived from different methods have been reported, but often separately across fuel types, burn phases, and experimental systems, limiting assessment of their consistency and controlling factors. Here, we compare κ derived from a cloud condensation nuclei counter (CCNC), a hygroscopicity tandem differential mobility analyzer (HTDMA), and AMS–SP2 composition-based predictions within a single laboratory framework. Fresh and aged biomass-burning particles were produced from four representative fuels: hardwood, softwood, peat, and leaves, burned under smouldering or flaming conditions. CCNC-derived κ systematically exceeded HTDMA-derived κ, with discrepancy magnitudes strongly dependent on fuel type and burn phase. Composition-based κ generally fell between CCNC and HTDMA values. Coupled effects of κ size dependence and size representation, together with externally mixed non-hygroscopic black carbon, affected discrepancies among the three approaches but only partly explained them. Using O:C-informed κ_org values instead of a constant κ_org = 0.1 consistently improved AMS–SP2 prediction accuracy relative to HTDMA values in peat experiments only, by 16–59 %, while substantial residual discrepancies remained. These results suggest the presence of additional controlling factors not tested here, motivating measurements of surface-active species, particle phase state, and co-condensation of soluble gases to potentially explain the remaining discrepancies in future work. They further highlight the need for regime-aware κ parameterizations in atmospheric and climate models, alongside careful treatment of size representation, black-carbon inclusion, and κ_org assumptions in composition-based hygroscopicity predictions.

The hygroscopicity parameter κ is important for assessing biomass-burning aerosol impacts on visibility, direct radiative forcing, and cloud-related indirect effects. Discrepancies among κ values derived from different methods have been reported, but often separately across fuel types, burn phases, and experimental systems, limiting assessment of their consistency and controlling factors. Here, we compare κ derived from a cloud condensation nuclei counter (CCNC), a hygroscopicity tandem differential mobility analyzer (HTDMA), and AMS–SP2 composition-based predictions within a single laboratory framework. Fresh and aged biomass-burning particles were produced from four representative fuels: hardwood, softwood, peat, and leaves, burned under smouldering or flaming conditions. CCNC-derived κ systematically exceeded HTDMA-derived κ, with discrepancy magnitudes strongly dependent on fuel type and burn phase. Composition-based κ generally fell between CCNC and HTDMA values. Coupled effects of κ size dependence and size representation, together with externally mixed non-hygroscopic black carbon, affected discrepancies among the three approaches but only partly explained them. Using O:C-informed κ_org values instead of a constant κ_org = 0.1 consistently improved AMS–SP2 prediction accuracy relative to HTDMA values in peat experiments only, by 16–59 %, while substantial residual discrepancies remained. These results suggest the presence of additional controlling factors not tested here, motivating measurements of surface-active species, particle phase state, and co-condensation of soluble gases to potentially explain the remaining discrepancies in future work. They further highlight the need for regime-aware κ parameterizations in atmospheric and climate models, alongside careful treatment of size representation, black-carbon inclusion, and κ_org assumptions in composition-based hygroscopicity predictions.

The hygroscopicity parameter κ is important for assessing biomass-burning aerosol impacts on visibility, direct radiative forcing, and cloud-related indirect effects. Discrepancies among κ values derived from different methods have been reported, but often separately across fuel types, burn phases, and experimental systems, limiting assessment of their consistency and controlling factors. Here, we compare κ derived from a cloud condensation nuclei counter (CCNC), a hygroscopicity tandem differential mobility analyzer (HTDMA), and AMS–SP2 composition-based predictions within a single laboratory framework. Fresh and aged biomass-burning particles were produced from four representative fuels: hardwood, softwood, peat, and leaves, burned under smouldering or flaming conditions. CCNC-derived κ systematically exceeded HTDMA-derived κ, with discrepancy magnitudes strongly dependent on fuel type and burn phase. Composition-based κ generally fell between CCNC and HTDMA values. Coupled effects of κ size dependence and size representation, together with externally mixed non-hygroscopic black carbon, affected discrepancies among the three approaches but only partly explained them. Using O:C-informed κ_org values instead of a constant κ_org = 0.1 consistently improved AMS–SP2 prediction accuracy relative to HTDMA values in peat experiments only, by 16–59 %, while substantial residual discrepancies remained. These results suggest the presence of additional controlling factors not tested here, motivating measurements of surface-active species, particle phase state, and co-condensation of soluble gases to potentially explain the remaining discrepancies in future work. They further highlight the need for regime-aware κ parameterizations in atmospheric and climate models, alongside careful treatment of size representation, black-carbon inclusion, and κ_org assumptions in composition-based hygroscopicity predictions.

The hygroscopicity parameter κ is important for assessing biomass-burning aerosol impacts on visibility, direct radiative forcing, and cloud-related indirect effects. Discrepancies among κ values derived from different methods have been reported, but often separately across fuel types, burn phases, and experimental systems, limiting assessment of their consistency and controlling factors. Here, we compare κ derived from a cloud condensation nuclei counter (CCNC), a hygroscopicity tandem differential mobility analyzer (HTDMA), and AMS–SP2 composition-based predictions within a single laboratory framework. Fresh and aged biomass-burning particles were produced from four representative fuels: hardwood, softwood, peat, and leaves, burned under smouldering or flaming conditions. CCNC-derived κ systematically exceeded HTDMA-derived κ, with discrepancy magnitudes strongly dependent on fuel type and burn phase. Composition-based κ generally fell between CCNC and HTDMA values. Coupled effects of κ size dependence and size representation, together with externally mixed non-hygroscopic black carbon, affected discrepancies among the three approaches but only partly explained them. Using O:C-informed κ_org values instead of a constant κ_org = 0.1 consistently improved AMS–SP2 prediction accuracy relative to HTDMA values in peat experiments only, by 16–59 %, while substantial residual discrepancies remained. These results suggest the presence of additional controlling factors not tested here, motivating measurements of surface-active species, particle phase state, and co-condensation of soluble gases to potentially explain the remaining discrepancies in future work. They further highlight the need for regime-aware κ parameterizations in atmospheric and climate models, alongside careful treatment of size representation, black-carbon inclusion, and κ_org assumptions in composition-based hygroscopicity predictions.

The hygroscopicity parameter κ is important for assessing biomass-burning aerosol impacts on visibility, direct radiative forcing, and cloud-related indirect effects. Discrepancies among κ values derived from different methods have been reported, but often separately across fuel types, burn phases, and experimental systems, limiting assessment of their consistency and controlling factors. Here, we compare κ derived from a cloud condensation nuclei counter (CCNC), a hygroscopicity tandem differential mobility analyzer (HTDMA), and AMS–SP2 composition-based predictions within a single laboratory framework. Fresh and aged biomass-burning particles were produced from four representative fuels: hardwood, softwood, peat, and leaves, burned under smouldering or flaming conditions. CCNC-derived κ systematically exceeded HTDMA-derived κ, with discrepancy magnitudes strongly dependent on fuel type and burn phase. Composition-based κ generally fell between CCNC and HTDMA values. Coupled effects of κ size dependence and size representation, together with externally mixed non-hygroscopic black carbon, affected discrepancies among the three approaches but only partly explained them. Using O:C-informed κ_org values instead of a constant κ_org = 0.1 consistently improved AMS–SP2 prediction accuracy relative to HTDMA values in peat experiments only, by 16–59 %, while substantial residual discrepancies remained. These results suggest the presence of additional controlling factors not tested here, motivating measurements of surface-active species, particle phase state, and co-condensation of soluble gases to potentially explain the remaining discrepancies in future work. They further highlight the need for regime-aware κ parameterizations in atmospheric and climate models, alongside careful treatment of size representation, black-carbon inclusion, and κ_org assumptions in composition-based hygroscopicity predictions.

The hygroscopicity parameter κ is important for assessing biomass-burning aerosol impacts on visibility, direct radiative forcing, and cloud-related indirect effects. Discrepancies among κ values derived from different methods have been reported, but often separately across fuel types, burn phases, and experimental systems, limiting assessment of their consistency and controlling factors. Here, we compare κ derived from a cloud condensation nuclei counter (CCNC), a hygroscopicity tandem differential mobility analyzer (HTDMA), and AMS–SP2 composition-based predictions within a single laboratory framework. Fresh and aged biomass-burning particles were produced from four representative fuels: hardwood, softwood, peat, and leaves, burned under smouldering or flaming conditions. CCNC-derived κ systematically exceeded HTDMA-derived κ, with discrepancy magnitudes strongly dependent on fuel type and burn phase. Composition-based κ generally fell between CCNC and HTDMA values. Coupled effects of κ size dependence and size representation, together with externally mixed non-hygroscopic black carbon, affected discrepancies among the three approaches but only partly explained them. Using O:C-informed κorg values instead of a constant κorg = 0.1 consistently improved AMS–SP2 prediction accuracy relative to HTDMA values in peat experiments only, by 16–59 %, while substantial residual discrepancies remained. These results suggest the presence of additional controlling factors not tested here, motivating measurements of surface-active species, particle phase state, and co-condensation of soluble gases to potentially explain the remaining discrepancies in future work. They further highlight the need for regime-aware κ parameterizations in atmospheric and climate models, alongside careful treatment of size representation, black-carbon inclusion, and κorg assumptions in composition-based hygroscopicity predictions.