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Ecoacoustic Study Design Variation: Impact on Acoustic Indices and AudioSet Fingerprints

Description: Acoustic Index and AudioSet Fingerprint data quantified derived from audio recorded between the 26th of February and the 2nd March 2019. The original raw audio was compressed, shortened and temporally subset to replicate common inconsistencies in ecoacoustic studies. This data frame show how this experimental variation affects how soundscapes are quantified by Analytical Indices and the AudioSet FingerprintProject: This dataset was collected as part of the following SAFE research project: <a href="http://127.0.0.1:8000/projects/project_view/200">3D Acoustics for Audio Monitoring of Rainforest Biodiversity </a>Funding: These data were collected as part of research funded by: <ul><li>NERC (NERC QMEE CDT Studentship, NE/P012345/1, <a href="http://gotw.nerc.ac.uk/list_full.asp?pcode=NE%2FP012345%2F1&cookieConsent=A">http://gotw.nerc.ac.uk/list_full.asp?pcode=NE%2FP012345%2F1&cookieConsent=A</a>)</li></ul>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.XML metadata: GEMINI compliant metadata for this dataset is available <a href="http://127.0.0.1:8000/datasets/xml_metadata?id=5153193">here</a>Files: This consists of 1 file: Ecoacoustic_Method_Comparison.xlsxEcoacoustic_Method_Comparison.xlsxThis file contains dataset metadata and 2 data tables:<ol><li>Analytical index values under differing experimental conditions (described in worksheet Analytical_Index_Data)Description: This dataset contains all the analytical indices derived from audio under different experimenatal conditionsNumber of fields: 16Number of data rows: 87211Fields: <ul><li>id.no: Sample ID (Field type: id)</li><li>file.size: File size as a % of uncompressed (Field type: numeric)</li><li>compression: Compression level (Mp3) (Field type: ordered categorical)</li><li>frame.size: Frame size (recording length) (Field type: ordered categorical)</li><li>site: Field Site Location (Field type: location)</li><li>req.freq: Recording Frequency (Field type: numeric)</li><li>date: Date (Field type: date)</li><li>time: Time ID (including subsamples) (Field type: id)</li><li>max.freq: Nyquist (Maximum) frequency (Field type: numeric)</li><li>ACI: Acoustic Complexity Index (Field type: numeric)</li><li>ADI: Acoustic Diversity Index (Field type: numeric)</li><li>Aeev: Acoustic Eveness (Field type: numeric)</li><li>Bio: Biodiversity Index (Field type: numeric)</li><li>H: Acoustic Entropy (Field type: numeric)</li><li>M: Median of Acoustic Envelope (Field type: numeric)</li><li>NDSI: Normalised Difference Soundscape Index (Field type: numeric)</li></ul></li><li>AudioSet Fingerprint values under differing experimental conditions (described in worksheet AudioSet_Fingerprint_Data)Description: This dataset contains all the audioset fingerprint values derived from audio under different experimenatal conditionsNumber of fields: 137Number of data rows: 87329Fields: <ul><li>id.no: Sample ID (Field type: id)</li><li>file.size: File size as a % of uncompressed (Field type: numeric)</li><li>frame.size: Frame size (recording length) (Field type: ordered categorical)</li><li>compression: Compression level (Mp3) (Field type: ordered categorical)</li><li>site: Field Site Location (Field type: location)</li><li>req.freq: Recording Frequency (Field type: numeric)</li><li>date: Date (Field type: date)</li><li>time: Time ID (including subsamples) (Field type: id)</li><li>max.freq: Nyquist (Maximum) frequency (Field type: numeric)</li><li>feat1: Feature 1 (Field type: numeric)</li><li>feat2: Feature 2 (Field type: numeric)</li><li>feat3: Feature 3 (Field type: numeric)</li><li>feat4: Feature 4 (Field type: numeric)</li><li>feat5: Feature 5 (Field type: numeric)</li><li>feat6: Feature 6 (Field type: numeric)</li><li>feat7: Feature 7 (Field type: numeric)</li><li>feat8: Feature 8 (Field type: numeric)</li><li>feat9: Feature 9 (Field type: numeric)</li><li>feat10: Feature 10 (Field type: numeric)</li><li>feat11: Feature 11 (Field type: numeric)</li><li>feat12: Feature 12 (Field type: numeric)</li><li>feat13: Feature 13 (Field type: numeric)</li><li>feat14: Feature 14 (Field type: numeric)</li><li>feat15: Feature 15 (Field type: numeric)</li><li>feat16: Feature 16 (Field type: numeric)</li><li>feat17: Feature 17 (Field type: numeric)</li><li>feat18: Feature 18 (Field type: numeric)</li><li>feat19: Feature 19 (Field type: numeric)</li><li>feat20: Feature 20 (Field type: numeric)</li><li>feat21: Feature 21 (Field type: numeric)</li><li>feat22: Feature 22 (Field type: numeric)</li><li>feat23: Feature 23 (Field type: numeric)</li><li>feat24: Feature 24 (Field type: numeric)</li><li>feat25: Feature 25 (Field type: numeric)</li><li>feat26: Feature 26 (Field type: numeric)</li><li>feat27: Feature 27 (Field type: numeric)</li><li>feat28: Feature 28 (Field type: numeric)</li><li>feat29: Feature 29 (Field type: numeric)</li><li>feat30: Feature 30 (Field type: numeric)</li><li>feat31: Feature 31 (Field type: numeric)</li><li>feat32: Feature 32 (Field type: numeric)</li><li>feat33: Feature 33 (Field type: numeric)</li><li>feat34: Feature 34 (Field type: numeric)</li><li>feat35: Feature 35 (Field type: numeric)</li><li>feat36: Feature 36 (Field type: numeric)</li><li>feat37: Feature 37 (Field type: numeric)</li><li>feat38: Feature 38 (Field type: numeric)</li><li>feat39: Feature 39 (Field type: numeric)</li><li>feat40: Feature 40 (Field type: numeric)</li><li>feat41: Feature 41 (Field type: numeric)</li><li>feat42: Feature 42 (Field type: numeric)</li><li>feat43: Feature 43 (Field type: numeric)</li><li>feat44: Feature 44 (Field type: numeric)</li><li>feat45: Feature 45 (Field type: numeric)</li><li>feat46: Feature 46 (Field type: numeric)</li><li>feat47: Feature 47 (Field type: numeric)</li><li>feat48: Feature 48 (Field type: numeric)</li><li>feat49: Feature 49 (Field type: numeric)</li><li>feat50: Feature 50 (Field type: numeric)</li><li>feat51: Feature 51 (Field type: numeric)</li><li>feat52: Feature 52 (Field type: numeric)</li><li>feat53: Feature 53 (Field type: numeric)</li><li>feat54: Feature 54 (Field type: numeric)</li><li>feat55: Feature 55 (Field type: numeric)</li><li>feat56: Feature 56 (Field type: numeric)</li><li>feat57: Feature 57 (Field type: numeric)</li><li>feat58: Feature 58 (Field type: numeric)</li><li>feat59: Feature 59 (Field type: numeric)</li><li>feat60: Feature 60 (Field type: numeric)</li><li>feat61: Feature 61 (Field type: numeric)</li><li>feat62: Feature 62 (Field type: numeric)</li><li>feat63: Feature 63 (Field type: numeric)</li><li>feat64: Feature 64 (Field type: numeric)</li><li>feat65: Feature 65 (Field type: numeric)</li><li>feat66: Feature 66 (Field type: numeric)</li><li>feat67: Feature 67 (Field type: numeric)</li><li>feat68: Feature 68 (Field type: numeric)</li><li>feat69: Feature 69 (Field type: numeric)</li><li>feat70: Feature 70 (Field type: numeric)</li><li>feat71: Feature 71 (Field type: numeric)</li><li>feat72: Feature 72 (Field type: numeric)</li><li>feat73: Feature 73 (Field type: numeric)</li><li>feat74: Feature 74 (Field type: numeric)</li><li>feat75: Feature 75 (Field type: numeric)</li><li>feat76: Feature 76 (Field type: numeric)</li><li>feat77: Feature 77 (Field type: numeric)</li><li>feat78: Feature 78 (Field type: numeric)</li><li>feat79: Feature 79 (Field type: numeric)</li><li>feat80: Feature 80 (Field type: numeric)</li><li>feat81: Feature 81 (Field type: numeric)</li><li>feat82: Feature 82 (Field type: numeric)</li><li>feat83: Feature 83 (Field type: numeric)</li><li>feat84: Feature 84 (Field type: numeric)</li><li>feat85: Feature 85 (Field type: numeric)</li><li>feat86: Feature 86 (Field type: numeric)</li><li>feat87: Feature 87 (Field type: numeric)</li><li>feat88: Feature 88 (Field type: numeric)</li><li>feat89: Feature 89 (Field type: numeric)</li><li>feat90: Feature 90 (Field type: numeric)</li><li>feat91: Feature 91 (Field type: numeric)</li><li>feat92: Feature 92 (Field type: numeric)</li><li>feat93: Feature 93 (Field type: numeric)</li><li>feat94: Feature 94 (Field type: numeric)</li><li>feat95: Feature 95 (Field type: numeric)</li><li>feat96: Feature 96 (Field type: numeric)</li><li>feat97: Feature 97 (Field type: numeric)</li><li>feat98: Feature 98 (Field type: numeric)</li><li>feat99: Feature 99 (Field type: numeric)</li><li>feat100: Feature 100 (Field type: numeric)</li><li>feat101: Feature 101 (Field type: numeric)</li><li>feat102: Feature 102 (Field type: numeric)</li><li>feat103: Feature 103 (Field type: numeric)</li><li>feat104: Feature 104 (Field type: numeric)</li><li>feat105: Feature 105 (Field type: numeric)</li><li>feat106: Feature 106 (Field type: numeric)</li><li>feat107: Feature 107 (Field type: numeric)</li><li>feat108: Feature 108 (Field type: numeric)</li><li>feat109: Feature 109 (Field type: numeric)</li><li>feat110: Feature 110 (Field type: numeric)</li><li>feat111: Feature 111 (Field type: numeric)</li><li>feat112: Feature 112 (Field type: numeric)</li><li>feat113: Feature 113 (Field type: numeric)</li><li>feat114: Feature 114 (Field type: numeric)</li><li>feat115: Feature 115 (Field type: numeric)</li><li>feat116: Feature 116 (Field type: numeric)</li><li>feat117: Feature 117 (Field type: numeric)</li><li>feat118: Feature 118 (Field type: numeric)</li><li>feat119: Feature 119 (Field type: numeric)</li><li>feat120: Feature 120 (Field type: numeric)</li><li>feat121: Feature 121 (Field type: numeric)</li><li>feat122: Feature 122 (Field type: numeric)</li><li>feat123: Feature 123 (Field type: numeric)</li><li>feat124: Feature 124 (Field type: numeric)</li><li>feat125: Feature 125 (Field type: numeric)</li><li>feat126: Feature 126 (Field type: numeric)</li><li>feat127: Feature 127 (Field type: numeric)</li><li>feat128: Feature 128 (Field type: numeric)</li></ul></li></ol>Date range: 2019-02-26 to 2019-06-02Latitudinal extent: 4.6644 to 4.7027Longitudinal extent: 117.5351 to 117.5914

Data for "The Impact of Chromophore Choice on the Assembly Kinetics and Primary Photochemistry of a red/green Cyanobacteriochrome"

Data for the publication "The Impact of Chromophore Choice on the Assembly Kinetics and Primary Photochemistry of a red/green Cyanobacteriochrome" in PCCP

Replication package for "How does Migrating to Kotlin Impact the Run-time Efficiency of Android Apps?"

Replication Package This section briefly describes the contents of the replication package, which makes it possible to replicate this study. Dataset construction: The recreation of the GitLab AndroidTimeMachine instance is done using the snapshot related code found in the Kotlin program. This program uses a running instance of the AndroidTimeMachine’s Neo4j database and uses its data to make API calls to a running GitLab instance which imports the GitHub projects found. This step can be skipped, and a GitLab instance can be instantiated directly using our created Docker volumes. The is also capable of performing the Kotlin filtering steps. For this, it uses the results of the previous steps as input and clones each repository for performing the filtering procedure. SLOC counting: SLOC counting related scripts are found in and implement our methods by cloning the project and running CLOC. It also plots the line graphs that we manually categorized into evolution trends. The raw SLOC data is found in , and all of the plotted evolution trends are found in. Kotlin migration mining: The implementation for mining is located in , and implements our methods by cloning a repository from the running GitLab instance and executing described methods. All commits we found are located in. Run-time efficiency: As mentioned in the thesis, we used AndroidRunner. A fork of AndroidRunner we used for conducting our experiments is located in . In the folder , each configuration combined with raw results is found per test subject. The final data we used after removing corrupt frame times is located in the folder, which also contains . This python script implements our statistical analysis methods.

opencc-zeroJul 2022View details →

dryad36/100

Projected impacts of climate and land use changes on the habitat of Atlantic Forest plants in Brazil

Aim: To provide novel evidence on the average impact of climate and land use changes on habitat suitability for tropical plants and to test previous conclusions on the relative importance of these two drivers in shaping future availability of habitat for tropical plant species. Location: Brazil's Atlantic Forest domain. Time period: Plant occurrences recorded between 1960 and 2014. Baseline climate from 1960-2000 and land use from 2015. Projected scenarios of climate for 2041-2060 and land use for 2050. Major taxa studied: Angiosperms. Results: Our results suggest that climate change alone will, surprisingly, have only a modest negative impact on the mean habitat suitability, decreasing it by 2% (median = -5% to -7%, variation associated with scenarios). Land use change alone had a more consistent negative impact on habitat suitability, causing mean and median reductions of 4% to 6%. When the effects of climate and land use are combined, the mean habitat suitability was reduced by 4% (median = -9% to -11%). Main conclusions: The combined impacts of climate and land use changes were substantial, although smaller than expected. Habitat suitability decreased for most species, but it increased substantially for some species, suggesting that the distribution of impacts across species is markedly right skewed. The impacts were typically detrimental to small-ranged species and neutral or beneficial to widespread species. Land use change rather than climate change will likely cause more losses to the habitat of Atlantic Forest plant species within the next several decades.

opencc-by-4.0Jul 2021View details →

Impact of RbF and NaF Postdeposition Treatments on Charge Carrier Transport and Recombination in Ga-Graded Cu(In,Ga)Se2 Solar Cells

Excel file with data to all figures published in our article found at <a href="https://doi.org/10.1002/adfm.202103663">https://doi.org/10.1002/adfm.202103663  </a>(Advanced Functional Materials)

WACCMX+DART output files associated with the publication "The Impact of Assimilating COSMIC-2 Observations of Electron Density in WACCMX"

WACCMX+DART output files that are associated with the publication "The Impact of Assimilating COSMIC-2 Observations of Electron Density in WACCMX". Data files include DART diagnostics for the COSMIC-2 and Control experiments, as well as the vertical plasma drift velocities.

opencc-by-4.0Oct 2020View details →

SLR Artifacts - CONTINUOUS INTEGRATION QUALITY IMPACTS

SLR Artifacts - CONTINUOUS INTEGRATION QUALITY IMPACTS <ul> <li>ODS file SLR - Studies.ods containing the selected papers in each research step.</li> <li>ODS file SLR - Claims.ods containing the extracted claims in the papers, the coding process, and themes.</li> <li>SQL file to restore a relational database with the relations: studies (included ones), claims, codes, themes, and authors.</li> <li>CI SLR - Quality Assessment.pdf - containing the quality assessment checklist.</li> <li>scripts_search.zip -  containing the scripts used to perform the search of papers.</li> </ul>

Datasets and scripts to reproduce figures from the paper entitled "Impact of Chlorophyll Shading on the Peruvian Upwelling System" by Echevin et al.

This dataset can be used to reproduce the 4 figures published in the paper entitled "Impact of Chlorophyll Shading on the Peruvian Upwelling System" by Echevin et al. The scripts must be run using ferret software as follows: > go script_figureX.jnl The netcdf files are described below: - model NSH and SH climatology (3D fields :x,y,time): Peru10km_SHADING_monthlyclim_Y16-Y25.nc Peru10km_NOSHADE_monthlyclim_Y16-Y25.nc - NSH and SH model along-shore averaged climatologies (3D fields: x,z,time): section_ave.mean.NOSH10km.8S13S.nc section_ave.mean.SH10km.8S13S.nc - model grid: Peru10km_grd.nc - 2-degrees-wide coastal mask for the grid: mask_Peru10km_w2deg.nc

opencc-zeroAug 2021View details →

dryad36/100

Role of species richness and human-impacts in resisting invasive species in tropical forests

The biotic resistance hypothesis suggests that biodiversity rich areas should be resistant to biological invasions. Globally, conservationists use this hypothesis to protect diverse ecosystems. However, supporting data are often contradictory, possibly due to several confounding factors. Complexity in inferences increase in the tropics, which are sparsely studied. We hypothesize that human impacts, forest type and climate would modulate the relationship between native and invasive plant richness. To understand these interacting and varying effects of native richness and human disturbance on plant invasions, we sampled 354 grids of 25 km2 with equal representation of protected areas and multi-use areas to record abundance of native and non-native plants from 34 protected areas across five forest types in tropical India. We used linear mixed effect models to investigate occurrence and abundance of invasive plants with respect to varying native richness, human impacts, forest types and climate. Human use of forests increased richness and abundance of invasive plants across all forest types. After accounting for human-use, native species richness of tropical wet forests had a negative relationship with invasive plants richness and abundance, while the relationship reversed with increasing aridity and temperature. Human infrastructure facilitated invasions within protected areas. Synthesis. The biotic resistance hypothesis explained a lower number of invasions within protected tropical wet forests but not within dry forests. Human-free protected areas had lower richness and abundance of invasive plants across all systems, especially in wet tropical forests. Our results support the contextual importance of the biotic resistance hypothesis, while stressing the importance of protected areas, insulated from human impacts, to preserve the integrity of vulnerable natural systems.

dryad36/100

Within-year and among-year variation in impacts of targeted conservation management on juvenile survival in a threatened population

1. Overall impacts of targeted conservation interventions on population growth rate (λ) will depend on within-year and among-year variation in exposure of target individuals to interventions, and in intervention efficacy in increasing vital rates of exposed individuals. Juvenile survival is one key vital rate that commonly varies substantially within and among years, and consequently drives variation in λ. However, within-year, among-year and overall impacts of targeted interventions on population-wide survival probabilities of potentially mobile juveniles are rarely quantified, precluding full evaluation and evidence-based refinement of interventions. 2. We applied multi-state mark-recapture models to eight years of ring-resighting data from a threatened red-billed chough (Pyrrhocorax pyrrhocorax) population to quantify within-year and among-year variation in juvenile exposure to a targeted intervention of supplementary feeding and parasite treatment, and to estimate efficacy in increasing juvenile survival probability. We then combined and up-scaled these estimated effects to evaluate the impact of the eight-year intervention on overall population-wide survival probability and resulting population size. 3. High proportions of surviving juveniles (>70%) were exposed to the intervention across the annual biological cycle in all years. Exposure was associated with higher short-term survival probabilities through the full annual cycle. Consequently, management increased estimated population-wide annual juvenile survival by approximately 0.14. However, such effects were only evident in cohorts with low overall annual survival. 4. Population models projected that these impacts on annual juvenile survival substantially reduced population decline, such that population size at the end of the eight-year intervention was approximately double that without management. 5. Synthesis and applications. Our results show how complex patterns of within-year and among-year variation in exposure and efficacy of targeted conservation interventions can arise and scale up to affect population-level outcomes. We demonstrate positive effects of a major intervention, but also highlight potential routes to improve efficacy, for example through more precise targeting of agricultural management actions in the context of among-year variation in environmental conditions.

opencc-zeroAug 2021View details →

Figure 4 in Impacts of food-based enrichment on behaviour and physiology of male greater rheas (Rhea Americana, Rheidae, Aves)

Figure 4. Spearman correlation between daily faecal glucocorticoid metabolite concentration and daily number of recordings of "pacing" behaviour of greater rheas during the enrichment phase in the enclosure at the Belo Horizonte Zoo, Brazil (rs = 0.418; p <0.05, N = 29; df = 2).

Figure 2 in Impacts of food-based enrichment on behaviour and physiology of male greater rheas (Rhea Americana, Rheidae, Aves)

Figure 2. Exhibition of behaviours "walking" (F = 31.51, p <0.01, N = 30, DF = 2), "foraging" (F = 28.31, p <0.01, N = 30, DF = 2), "eating faeces" (F = 6.01, p = 0.05, N = 30, DF = 2) and "pacing" (F = 32.06, p <0.01, N = 30, DF = 2) by greater rheas in the three phases of a food enrichment study in the enclosure at the Belo Horizonte Zoo, Brazil. Different letters represent treatments that significantly differed between each other. b = baseline; e = enrichment; p = post-enrichment.

Figure 1 in Impacts of food-based enrichment on behaviour and physiology of male greater rheas (Rhea Americana, Rheidae, Aves)

Figure 1. Graphic timeline for the recording of behavioural and physiological data of greater rheas. Each phase lasted 30 days, with intervals of 14 days among phases. Behavioural and physiological data occurred concomitantly. In this timeline, it is represented data collection that occurred on Tuesdays and Thursdays. In Mondays, Wednesdays and Fridays, data collection occurred from 13:00 h to 16:15 h. Enrichment was provided at 08:00 or 13:00 h, depending on the day of the week.

Figure 3. GCM concentrations for greater rheas during a in Impacts of food-based enrichment on behaviour and physiology of male greater rheas (Rhea Americana, Rheidae, Aves)

Figure 3. GCM concentrations for greater rheas during a food enrichment study at the Belo Horizonte Zoo, Brazil (F = 22.51; p <0.01; N = 27; df = 2). Different letters represents phases that differed significantly between each other.

Tunable Resistive Pulse Sensing data of "The impact of storage on extracellular vesicles: a systematic study" experiments

Raw data of Tunable Resistive Pulse Sensing (TRPS) of "The impact of storage on extracellular vesicles: a systematic study" experiments

Machine learning-based evidence and attribution mapping of 100,000 climate impact studies - Data

Data for the paper Machine learning-based evidence and attribution mapping of 100,000 climate impact studies Document Metadata 0c_doc_info.csv contains basic document metadata for each document considered in our study Predictions In each predictions file, 1 refers to a document hand-labelled as belonging to a category, and 0 refers to a document hand-labelled as not belonging to a category. All values in between are predicted values, where for values greater than 0.5, a document is considered likely to belong to the given category. 1_document_relevance.csv contains the predicted relevance of a document to the study. 1_driver_predictions.csv contains the predicted climate driver of each document. 1_impact_predictions.csv contains the predicted impact type of each document Geographical data Place_df.csv contains a row for each geographical entity automatically extracted from each study Study_gridcell_2.5.csv contains a row matching each study with each grid cell covered by the study’s smallest mentioned geographical entity Merged data 2_study_da.csv contains a row for each study describing the aggregated detection and attribution characteristics of the grid cells the study refers to 2_merged_da_data.csv contains a row for each grid cell describing the attribution categories and the number of weighted grid cells for each climate driver.

Flow cytometry data of "The impact of storage on extracellular vesicles: a systematic study" experiments

Flow cytometry raw data acquired for the pubblication of "The impact of storage on extracellular vesicles: a systematic study"

COV-BHP - Psychological Impact of the COVID-19 Outbreak on Health Professionals

The COVID-19 pandemic had a massive impact on health care systems, increasing the risks of psychological distress in health professionals. This database includes data from a study which assessed the prevalence of burnout and psychopathological conditions in health professionals working in a health institution in the Northern Italy, and identified socio-demographic, work-related and psychological predictors of burnout. Health professionals working in the hospitals of the Istituto Auxologico Italiano were asked to participate to an online anonymous survey investigating socio-demographic data, COVID-19 emergency-related work and psychological factors, state anxiety, psychological distress, post-traumatic symptoms and burnout.

opencc-by-4.0Jun 2021View details →

Impacts of biomass burning in peninsular Southeast Asia on PM2.5 concentration and ozone formation in southern China during springtime – A case study

Abstract: Biomass burning (BB) affects fine particulate matter (PM2.5) and ozone (O3) formations by emitting their gaseous precursors and primary aerosols. Impacts of BB in peninsular Southeast Asia (BB-PSEA) are evaluated on the PM2.5 and O3 formations in southern China, using a source-oriented WRF-Chem model to simulate an air pollution episode from 21 to 25 March 2015. The source-oriented model separates the emission from the BB-PSEA and other sources and is able to evaluate the effect of aerosol-radiation interactions (ARI) and aerosol-photolysis interactions (API) from the BB-PSEA. Comparisons with observations reveal that the model performs well in simulating the air pollution episode. Sensitivity experiments show that BB-PSEA increases PM2.5 concentrations by 39.3 μg m-3 (68.0%) in Yunnan Province (YNP) and 8.4 μg m-3 (24.1%) in other downwind areas (ODA) in southern China (including the provinces of Guizhou, Guangxi, Hunan, Guangdong, Jiangxi, Fujian, and Zhejiang) on the regional average. The PM2.5 enhancement is mainly contributed by primary aerosols in YNP but by secondary aerosols in the ODA. The BB-PSEA increases O3 concentrations of 18.1 μg m-3 (19.4%) in YNP and decreases O3 concentrations in the ODA by 3.7 μg m-3 (5.3%). The O3 increase in YNP is contributed by the gaseous emissions of the BB-PSEA, and the O3 decrease in the ODA is caused by the effects of ARI and API of the BB-PSEA. The NH3 emissions from the BB-PSEA plays a key role in enhancing secondary inorganic aerosols in southern China, and also determine the PM2.5 increase in the ODA.