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The research was carried out on the basis of direct measurements in the surroundings of four selected working coal-fired power plants and four working coking plants. The samples of suspended dust PM10, respirable fraction PM2.5 and submicron particulate matter PM1 were collected in the surroundings of power generation facilities and in the surroundings of coking plants.Location of measurement pointsThe location of the measurement points was selected in southern Poland, around the selected four working coal-fired power plants and four working coking plants. The sampling points in the surroundings of the power plant (P1, P2, P3 and P4) and the coking plant (K1, K2, K3 and K4) were located at the distance of approximately 2 km to the north-east from the respective object (Fig. 1).Figure 1Location of the sampling sites (the map was generated based on data from the BDL18 website).Full size imageThe location of the measurement points was a compromise, taking into account the representativeness of the receptor, the possibility to connect the testing equipment and the consent of the property owners. To eliminate the impact of a heating season, and especially that of low emissions, presented in the studies by19, the measurement sessions were carried out only in the summer season. The samples of particulate matter were collected on a weekly basis, with 4 sessions at one site. The methodology applied in this work is presented in20,21. The location of measurement sites:

point P1: 50° 08′ 37.87″ N; 18° 32′ 15.76″ (Golejów—a suburban district of Rybnik in the Śląskie Voivodeship, in the vicinity of a working power plant with a capacity of 1775 MW; population:

2 300);

point P2: 50° 45′ 35.41″ N; 17° 56′ 20.43″ E (Świerkle—a rural area in the Opolskie Voivodeship (Dobrzeń Wielki commune) near a working power plant with a capacity of 1,492 MW; population: 520);

point P3: 50° 12′ 33.46″ N; 19° 28′ 28.77″ E (Czyżówka—rural area in the Małopolskie Voivodeship (commune of Trzebinia) near a working power plant with a capacity of 786 MW; population: 700);

point P4: 50° 13′ 48.90″ N; 19° 13′ 24.45″ E (suburbs of Jaworzno (Śląskie Voivodeship) in the vicinity of a 1,345 MW power plant; number of inhabitants: 95 500);

K1 point: 50° 10′ 11.36″ N; 18° 40′ 34.35″ E (Czerwionka—Leszczyny in the Śląskie Voivodeship, in the vicinity of a small coking plant; number of inhabitants: 27 300);

K2 point: 50° 3′ 19.76″ N; 18° 30′ 21.69″ E (Popielów—a suburban district of Rybnik in the Śląskie Voivodeship, surrounded by a small working coking plant; population:3 300);

K3 point: 50° 21′ 24.08″ N; 19° 21′ 37.46″ E (Łęka—Dąbrowa Górnicza district, in the Śląskie Voivodeship, surrounded by a large coking plant; number of inhabitants: 700);

K4 point: 50° 21′ 0.47″ N; 18° 53′ 15.44″ E (Bytom—a city in the Śląskie Voivodeship, a small coking plant located on the outskirts of the city; population: 174 700).

The state of air pollution with particulate matter in the area investigated in the study is affected by various local sources of pollution emissions. At the measurement sites P1, P2, P3 and P4, the emissions are mainly from power plant chimneys, but also from auxiliary processes, i.e. coal storage and its transport. In addition, the recorded emissions are also influenced by other industrial plants operating in the vicinity of the measurement sites, domestic and municipal sector and the impact of automotive industry. The measurement sites K1, K2, K3 and K4 involve primarily the emissions accompanying the processes of coal coking as well as auxiliary processes, i.e. coal deposition, its transmission, management of products and post-production wastes. Additionally, they are affected by the emissions from industrial plants and low emission sources operating in this area, as well as the emission from the combustion of solid fuels for domestic or municipal purposes, as well as by the automotive industry.Sampling processThe samples of suspended dust (PM10), respirable fraction (PM2.5) and submicron particulate matter (PM1) were collected using the Dekati PM10 cascade impactor serial No. 6648 by Dekati (Finland) with the air flow rate of (1.8 {mathrm{m}}^{3}/mathrm{h}). The impactor Dekati PM10 guarantees the collection of dust samples for three cutpoint diameters: 10 μm, 2.5 μm and 1 μm. For the sampling at the first, second and third stages of the impactor, polycarbonate filters were used (Nuclepore 800 203, with the diameter of 25 mm, by Whatman International Ltd., Maidstone, UK). At the fourth stage, the dust was collected on a Teflon filter for particles ≤ 1 μm in diameter (Pall Teflo R2PJ047, 47 mm in diameter, by Pall International Ltd., New York, NY, USA). The average volume of air passing through the filters was approximately 300 m3. The impactor’s capture efficiency was characterized by the uncertainty below 2.8%. The mass of dust collected at the individual stages of the impactor was determined by the gravimetric method, and it was referenced to the volume of passed air (left(mathrm{mu g}/{mathrm{m}}^{3}right)) according to the PN-EN1234122. All impactor samples were analysed by inductively coupled plasma mass spectrometry (ICP-MS).The samples were collected at a height of 1.5 m from the ground, i.e. in the breathing zone for people. The respective dust fractions were collected in 7-day cycles from 28 May to 24 September 2014 (16 weeks) in the surroundings of four working coal-fired power plants and from 4 May to 28 August 2015 (16 weeks) in the surroundings of four working coking plants. The measurement campaign comprised four measurement sessions separately for each sampling site. One session comprised dust sampling at each stage of the Dekati PM10 cascade impactor and filters used for reference. The filters were taken back after study period and labeled during the collection process in the field and stored in the plastic containers for safe transportation and storage in laboratory for further analysis.In each measurement session, blind filters were stored at the sampling site, but they were not subjected to exposure. The sample data were corrected from these blanks. The length of the measurement cycles was conditioned by the need to collect an appropriate amount of research material (with the aerodynamic diameter of the dust grains  10 μm). Analogous (7-day) periods of dust sampling were used in the studies by4,23.Polycarbonate and Teflon filters were conditioned before and after dust collection at a temperature of 20 ± 1 °C (relative humidity 50%(pm ) 5%) for 48 h, and then weighed on a microbalance with an accuracy of 1 (mathrm{mu g}) (MXA5/1, by RADWAG, Poland).Taking into account the measurement sessions at four sites in the surroundings of the power plant (P1 (div) P4) and at four sites in the surroundings of the coking plant (K1 (div) K4), the aggregate number of samples exceeded 450.Chemical analysisThe qualitative and quantitative analysis of the obtained solutions was performed by inductively coupled plasma mass spectrometry using an ICP-MS instrument (NexION 300D, PerkinElmer, Inc., Waltham, MA, USA). For all elements determined simultaneously, the same parameters of the instrument were used, which are presented in the publications20,21,24.As standards for the determination of 75As, 111Cd, 59Co, 53Cr, 200Hg, 55Mn, 60Ni, 206Pb, 121Sb and 82Se, we applied the 1000 (mathrm{mu g}/{mathrm{cm}}^{3}) CertPUR ICP multi-element standard solution VI for ICP-MS by Merck, Germany. Ten repetitions were performed for all samples. The determined limits of detection (LOD) were based on 10 independent measurements for blank test. For the results obtained in that way, the mean value and the value of the standard deviation SD were calculated. The values of LOD for individual elements were determined on the basis of the dependence (1):$$mathrm{LOD}= {mathrm{x}}_{mathrm{sr}}+ 3mathrm{SD}$$
(1)

where: xśr—mean concentration value of the element, (mathrm{g}/{mathrm{dm}}^{3}), SD—standard deviation.The determination correctness of the content of the elements was verified with the use of certified reference materials: European Reference Material ERM-CZ120 and Standard Reference Material SRM 1648a (National Institute of Standards and Technology, USA). The recovery with the use of the said certified reference materials was respectively as follows: As (111% for ERM-CZ120 and 96% for SRM 1648a), Cd (97% and 105%), Co (108% and 97%), Cr (103% and 94%), Mn (106% and 100%), Ni (107% and 102%), Pb (107% and 105%) and Sb (99% and 91%). The certified reference materials did not contain Hg or Se. More

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AnimalsThe experimental yaks were divided into four groups: 1-day old, 30-days-old, 180-days-old and adult. Three yaks were selected for each group, regardless of sex, and purchased from a local herdsmen in Haiyan County of Qinghai Province. All of the yaks showed a good nutritional status, and appeared healthy with no apparent diseases or conditions. The yaks were sacrificed by exsanguination in a slaughterhouse. The lungs were obtained immediately after the yak had died, and tissue samples were immediately collected from the diaphragmatic lobe of right lungs (to ensure that obvious blood vessels and the trachea were not gathered). The tissue samples were divided into three parts. One part was cut into 1 cm3 sections and fixed with 4% paraformaldehyde (PFA). The other two parts were cut into 1 mm3 pieces; one part was fixed with 2.5% glutaraldehyde, and the other was put into a freezing tube and placed into liquid nitrogen.Ethics statementThis study was approved by the Institutional Animal Care and Use Committee of Qinghai University (Xining, China). All methods were carried out in accordance with the ARRIVE guidelines and the Animal Ethics Procedures and Guidelines of the People’s Republic of China. No local regulations or laws were overlooked. All yaks used in this study were purchased from local farmers.Haematoxylin and eosin stainingLung tissue samples (1 cm3) were fixed in 4% PFA, dehydrated in 30%, 50%, 75%, 95% and 100% ethanol and then treated with xylene before embedding in paraffin. Paraffin-embedded lung tissues were cut into 4 µm sections. The sections were deparaffinized in xylene, and sections were stained either with haematoxylin and eosin (HE) (Y&K Bio, Xi’an, China) or Masson’s trichrome stain, to examine general morphology.ImmunohistochemistryThe unstained, deparaffinized sections were rinsed with Phosphate Buffered Saline with Twen-20 (PBST) 3 times for 5 min each time. Then, endogenous peroxidase was quenched using 3% peroxide-methanol at room temperature in the dark for 25 min, and then the samples were placed on a decolorizing shaking table 3 times, for 5 min each. The slides were then incubated with 3% foetal bovine serum (Sangon Biotech, Shanghai, China) at room temperature for 25 min. The serum was discarded, and rabbit anti-cattle CD34 and rabbit anti-CD34 polyclonal antibodies (Proteintech group, Wuhan, China) diluted in phosphate buffer saline (PBS) were added. CD34 is a transmembrane glycoprotein known as an angiogenesis marker. The sections were incubated in the primary antibodies overnight at 4 °C. Then, the sections were rinsed in Phosphate Buffered Saline with Twen-20 (PBST) (3 × 5 min), goat anti-rabbit IgG was added, and the sections were incubated for 30 min at 37 °C. 3,3-Diaminobenzidin (DAB) was added to the sections to visualise antibody binding, and the sections were washed 3 times in PBST. Haematoxylin was used to counterstain the nucleus prior to the samples being dehydrated and mounted.An Olympus BX51 microscope was used to take photomicrographs of the microstructures, images depict 1000× magnification. Transmission electron microscopy.The TEM lung tissue samples were processed using previously published methods16. Fresh lung samples (1 mm3) were fixed with glutaraldehyde (2.5%, 24 h) and postfixed with osmium tetroxide (1%, 2 h). The samples were dehydrated in a series of increasing concentrations of ethanol and embedded in Epon812. After preparing semithin sections, ultrathin sections were double stained with uranyl acetate and lead citrate. A 10,000× magnification was used to observe and photograph the sections with a JEM 1230 electron microscope (JEOL, Tokyo, Japan) set at 120 kV.Quantitative real-time PCR (qPCR)The gene expression levels in lung tissues from the yaks in the four age groups were analysed using qPCR. Total RNA was isolated with TRIzol® reagent (Invitrogen, CA, USA). cDNA was obtained by reverse transcription of total RNA using the SYBR PrimeScript RT reagent Kit with gDNA Eraser (Perfect Real Time; Takara, Dalian, China). The forward and reverse primers sequences for the qPCR are shown in Table 1. The genes expression levels were detected using TB Green™ Premix Ex Taq™ II (TIi RNaseH Plus; Takara, Dalian, China) according to the manufacturer’s instructions. The 2−ΔΔCT method was used to analyse the relative expression of target genes, and the housekeeping gene β-actin was used for normalization.Table 1 Primer sequences.Full size tableWestern blot analysisEqual amounts of proteins of yak lung tissue in different development stages were harvested. These proteins were separated on 10% polyacrylamide gels and transferred onto polyvinylidene difluoride (PVDF) membranes (Sangon Biotech, Shanghai, China). PVDF membranes were blocked in 10% non-fat (skimmed) milk for 3 h and then incubated in rabbit anti-VEGFA polyclonal antibody (OriGene, Maryland, USA) at 4 °C overnight. The membranes were then incubated with a goat anti-rabbit IgG antibody (Abcam, Cambridge, UK) for 2 h being washed 3 times (10 min / time) with Tris-buffered saline with Twen-20 (TBST; containing 0.1% Twen-20). All antibodies were diluted according to the manufacturer’s instructions. Immunoblots were analysed by autograph using a Gel Doc™ XR + Gel documentation system (BIO-RAD, California, USA).Statistical analysisThe experimental data are showed as the mean ± standard deviation (SD). The differences between the four groups were compared using one-way ANOVA. P values at less than 0.05 were considered significantly different. More

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CORRESPONDENCE
14 September 2021

Preventing spillover as a key strategy against pandemics

Neil M. Vora

 ORCID: http://orcid.org/0000-0002-4989-3108

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Nigel Sizer

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Aaron Bernstein

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Neil M. Vora

Conservation International, Arlington, Virginia, USA.

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Nigel Sizer

Preventing Pandemics at the Source Coalition, Mount Kisco, New York, USA.

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Aaron Bernstein

Boston Children’s Hospital, Boston, Massachusetts, USA.

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Most new infectious diseases result from the spillover of pathogens from animals, particularly wildlife, to people. Spillover prevention should not be dismissed in discussions on how best to address pandemics (see Nature 596, 332–335; 2021).The belief that we are powerless to prevent spillover is, unfortunately, endorsed by many in public health and government. Improved management of farmed animals, regulations on wildlife trade and conservation of tropical forests have all helped to prevent spillover and subsequent outbreaks, as well as boosting greenhouse-gas mitigation and wildlife conservation (see go.nature.com/2uqwx1u). Moreover, preventing spillover is cheap compared with the costs of a single pandemic (A. P. Dobson et al. Science 369, 379–381; 2020).Outbreak containment measures will always be necessary, especially for the most vulnerable people in resource-limited settings, because spillover can never be completely eliminated. But if prioritized alongside post-spillover initiatives, outcomes will be more cost-effective, scientifically informed and equitable.

Nature 597, 332 (2021)
doi: https://doi.org/10.1038/d41586-021-02427-4

Competing Interests
The authors declare no competing interests.

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The participants of the on-farm trialsThe farmers taking part in the trials own between 0.3 and 40 ha. Most of them were smallholders (less than 2 ha) and used to plant vegetable fields of around 300 m2 per crop. Two out of 233 participating farmers are female, farmers’ age ranges from 24 to 68 years. All farmers learned agriculture from their parents, 70% are literate. Farmers and fields were visited 10–12 times per trial. In 2018, we started with 112 farmer fields, but some farmers did not follow strictly the obligatory agricultural practices (e.g., concerning fertilizer, irrigation, harvest), some lost the entire or parts of fields (e.g., by flood, grazing livestock), therefore all assessments concerning 2018 include 99 farmer fields. In 2019, we started with 136 farmer fields, two farmers did not follow the agreed farming practices, so assessments for 2019 are based on 134 farmer fields.The design of participatory field trialsWe conducted 14 trials in 2018 and 17 in 2019, each trial encompasses five FAP fields and three control fields in neighbouring villages. Minimum distance between FAP fields and between FAP and control fields was two thousand metres for nearly all fields, at least more than one thousand metres. In the mountainous region we used pumpkin, zucchini and faba bean as main crops (two years), in oasis okra and zucchini (two years), faba bean and pumpkin (2019), in the semi-arid region melon, zucchini, pumpkin, eggplant and faba bean (two years) and in the region with adequate rainfall tomato, faba bean, zucchini and eggplant (two years) and pumpkin (2019). The main crops were selected by farmers and agricultural advisors of the respective regions, MHEP by farmers of the respective trials and researchers.Field size was 300 m2 as recommended for smallholders5 with a 75% zone for the main crop in both, FAP and control. Except for okra, the 75% zone had four cultivars with four replications in a randomized system as recommended as enhanced practice by farmers in the pilot project in Morocco27. For okra only two cultivars are available in Morocco and trials used only seeds accessible also for farmers. FAP fields employed the 25% zones for habitat enhancement, whereas control fields had the main crop also in this zone. We used coriander, basil, cumin, dill, anise, celery, sunflower, canola, flax, zucchini, okra, melon, tomato, green pepper, cucumber, Armenian cucumber, eggplant, chia, arugula, watermelon, pumpkin, grass pea, cultivated lupinus, alfalfa, clover, vetch, faba bean and wild lupinus as MHEP, per trial between four and eight different MHEP. As faba bean starts flowering in end of February in Morocco, MHEP were partly forage crops as they flower early. MHEP were seeded in a way that around 2/3 flowered at the same time as the main crop and 1/3 before or after to prolong the foraging season on site for flower visitors. The habitat enhancement zones included also nesting and water support out of local materials, e.g., hollow stems, wood and dry mud with holes.Field managementIn oasis, all fields were irrigated by gravity flow, in the other sites all farmers used drip irrigation. The amount of dung used is based on farmers’ decision and varies per region: semi-arid region 500 kg/300 m2, mountainous region 1000 kg/300 m2, oasis 1500 kg/300 m2 and region with adequate rainfall 3000 kg/300 m2. Soil analysis was conducted for all fields but does not explain the income gaps between FAP and control. Pesticides (mainly neonicotinoids and broad-spectrum insecticides) were prohibited during trials. In some urgent cases with permission of the plant protection specialist, one foliar insecticide application for pest management was accepted when pest density reached the economic threshold.Insect sampling and methods to analyse the dataThe taxa richness of flower visitors was assessed by a combination of transect net samplings and pan trappings. In each field, insects were sampled four times, once before the flowering of the main crop, twice during its flowering and once afterwards. Each sampling took two days for each trial (four fields per day). Two sets of three pan traps (blue, yellow and white) were located in each field at the beginning of the first day of sampling and were collected the second day after 24 h. The samplings in 75% zones consisted of walking along two twenty eight metres transect lines for five min each. In the 25% zones flower visitors were collected once along an 80 m transect line around the 75% zone for ten minutes. The flower visitors were collected and kept separately per MHEP, but the respective time needed was recorded and added to the transect. The insects were collected using both sweep nets and insect vacuums. All flower visitors were collected except Apis mellifera, Bombus terrestris and Xylocopa pubescens that were identified visually on site. The collected insects were first fainted with ethyl acetate and afterwards placed inside killing jars filled with cyanide, afterwards pinned and labelled. Wild bees were identified to the genus level using the most recent key for wild bees in Europe52. The other flower visitors were identified to genus level or to family level. Significance concerning diversity was measured by Wilcoxon test53.In the 75% zones, pest insects, predators and parasitoid wasps were collected four times. Per farmer field, four one-square-metre quadrates were randomly selected, within the quadrates ten randomly selected plants were beaten five times, so in total we used 320 crop samples per trial. In the 25% zones, the beating method was similarly used for each MHEP (five sample plants per MHEP). Specimen were collected in plastic bags and kept in plastic tubes containing 70% ethanol for conservation. Abundance of pests was estimated by counting the number (i) recorded on each sample crop. Pest reduction was calculated by the rate of pest reduction (%) using the following formula: % = (1− AFAP(i) / AControl(i)) × 100, where AFAP (i) is the average of the abundance in the FAP plot; AControl (i) is the average of the abundance in the control plot54.Economic assessmentsThe economic assessments use the same calculation as the pilot projects5,27: the number of fruits was counted and weighed. Investment costs in FAP and control fields are the same in the 75% zones. The income from the 75% zones was assessed by multiplying total weight with market price per kg. The income from the 25% zones of control fields was assessed by total produce weight multiplied by market price per kg; investment costs were deducted. The income of the 25% zone of FAP fields was computed by multiplying total weight with market price per kg of MHEP minus respective investment costs and minus 100 MAD (1.5 person days per FAP field) as calculated labour costs for harvesting MHEP, though in our trials, farmers harvested themselves.SimulationsThe simulation of potential FAP impacts on food security and sparing natural land for pollinator and biodiversity protection is based on following assumptions. Basis is the total production (2016–2017 differentiated per crop; provided by the Moroccan Ministry of Agriculture on request) for faba bean (share of harvested crop with green pods as in the experiments, 105,760 ton in 10,205 ha), zucchini and pumpkin (179,519 ton in 7539 ha), melon (618,588 ton in 20,163 ha), eggplant (52,966 ton in 1885 ha) and tomato (1,293,761 ton in 15,888 ha). We did not include okra due to lack of national production data. For the simulation on potential increase of production through smallholders (≤ 2 ha), we use 13% as share of smallholders in North Africa for vegetable production49. For the simulation of the land-saving potential through smallholders, we used 11% (North Africa, share of smallholders’ land for food crops)55.The formula used for the simulation on the potential FAP impacts on food security (PIFS) is:$${text{PIFS}}, = ,left( {{text{SSP}}*left( {{{1}} – upmu } right)} right), + ,left( {{text{SSP }}*upmu } right){text{ }}*left( {{text{1}}, + ,left( {{text{GFT }}*{text{TE}}} right)} right) – {text{SSP}}$$PIFS: Potential increase in crop production because of FAP (t), SSP: Smallholders’ share of production in (t), GFT: FAP production gain in farm trials (%), µ: the share of smallholder-producers adopting FAP, TE: Technology effectiveness.The GFT employed is 85,2% which represents the average FAP production gain of the vegetables used in the simulation process. For µ we used either 10%, 30% or 50% and for TE we assumed that smallholder-producers gain either 50% or 70% of the total production gain achieved in on-farm trials with smallholder-farmers since farmers will adapt MHEP and their planting to their personal preferences.The formula used for the simulation of potential land saving (PLS):$${text{PLS}} = (({text{SAP}} * {text{PIFS}})/{text{SSP}})-{text{SAP}}$$PLS: Potential land saving in ha, SAP: Smallholders’ area of production in ha. More