Plant SystemsPlants

Dynamic Accumulators Part Two

Identifying Plants of Interest

Dynamic accumulators are plants that actively accumulate appreciable amounts of useful nutrients. These plants are potentially useful for those involved in soil remediation, composting operations, dietary planning, ecosystem studies and more. In Part One I describe how the dynamic accumulators (and excluders) may be qualified. I recommend reading this despite the fact it is quite dry, as it will make the information in this article easier to understand and apply.

This article presents plant-average nutrient concentrations (Table 1). It then uses the averages to identify plants that are accumulators and excluders of a range of plant nutrients (Tables 2 – 12).

The potential of any plant for accumulating a specific nutrient is limited by that nutrient’s availability in the plant’s environment and/or the symbionts that increase uptake for that nutrient. Active transport mechanisms (see Part One) are only effective where nutrients are present; and symbiosis is only beneficial where beneficial symbionts are present. A broad range of support species (the soil food web) are involved in plant nutrient acquisition and should be considered in any soil restoration project. Some brief discussion is included.

All data was originally sourced from Dr James A Duke’s Phytochemical and Ethnobotanical database and anonymously compiled in spreadsheets here. Plant-average concentrations have been derived using highest concentration data for each species wherever a range was provided in the data: this was around 5% of all data. This artificially inflates the average but better outlines the potential of a species found to be above average – the dynamic accumulators.

In Table 1 below are columns headed ‘99% Lower’ & ‘99% Upper’. These describe the lower and upper bounds of confidence intervals for the plant-average concentrations. Statistically, I have confidence that 99% of the time the plant-average concentration will be within these lower and upper bounds. The higher or lower you go from those bounds, the greater the odds are you are looking at accumulators or excluders respectively. In this manner we can identify these plants with reasonable certainty. Note that the 99% confidence interval gets proportionately smaller when the sample size increases; the more data we have the more accurate we get.

When you have a plant of interest, collect as much nutrient data as you can find, standardise the data’s units converting all to ppm, average the results and then compare these to the averages in Table 1. Assess if they are accumulators, excluders, or average (passive transport, see Part One).


Table 1. Plant-average nutrient concentrations (ppm) + 99% confidence interval
Nutrient Average Median 99% Lower 99% Upper Samples
Nitrogen 16801 12936 11395 22207 52
Phosphorus 3706 2797 3244 4168 438
Potassium 22834 17800 20414 25254 347
Sulphur 2337 1368 1680 2994 90
Calcium 11244 7784 9946 12542 496
Magnesium 3488 2545 2983 3993 287
Silicon 392 72 107 677 75
Iron 374 174 299 449 427
Boron 46 32.5 35 57 122
Copper 21 12 14 29 213
Manganese 272 63 170 374 242


Tables 2 – 12 below describe ten accumulators and excluders of each nutrient listed in Table 1. The column heading Bf is the biome-concentration factor (see Part One). This is a ratio you may derive from getting a plant species nutrient concentration and dividing by the plant-average concentration for that nutrient. The plant-average concentrations in table 1 can be used to assess the Bf of any plant you have concentration data for. Bf gives you a multiple of the average e.g. Bf = 2 = 2 times the plant-average concentration.



Table 2. Biomeconcentration factors (Bf) of nitrogen dynamic accumulators and excluders.

Plant-average 16,801 ppm; 99% confidence interval 11395 – 22207 ppm; 52 samples.

Species Common name Part ppm Bf
Cucumis sativus  Cucumber Fruit 80,000 4.76
Anethum graveolens  Dill Plant 55,300 3.29
Brassica oleracea Cauliflower Flower 47,500 2.83
Phaseolus vulgaris Bean Fruit 41,000 2.44
Bromelia pinguin  Wild Pineapple Shoot 35,800 2.13
Momordica charantia  Bitter melon Fruit 33,800 2.01
Melilotus indica  Annual yellow clover Plant 33,600 2.00
Ilex paraguariensis  Mate Leaf 30,000 1.79
Amphicarpaea bracteata Hog peanut Shoot 26,500 1.58
Lycopersicon esculentum  Tomato Leaf 26,000 1.55
Cyphomandra betacea  Tree tomato Fruit 4,450 0.26
Malva neglecta  Common mallow Plant 4,200 0.25
Malus domestica  Apple Fruit 4,000 0.24
Malva sylvestris  High mallow Leaf 3,300 0.20
Pyrus communis  Pear Fruit 3,000 0.18
Annona muricata  Soursop Fruit 2,700 0.16
Annona cherimola Cherimoya Fruit 2,270 0.14
Passiflora edulis  Maracuya Plant 1,920 0.11
Ananas comosus  Pineapple Fruit 1,150 0.07
Spondias tuberosa  Imbu Fruit 1,100 0.07

Nitrogen is not originally sourced from soils, rather, from Rhizobium & other nitrogen-fixing bacteria; but the accumulators in Table 2 are not all associated with nitrogen-fixers. The overall trend of nutrients as observed here still applies: When considering the data without nitrogen-fixers, soil nitrogen appears to be captured more efficiently by some plants than others.

The excesses in this data do not seem excessive when compared to other nutrients in this study. However, the sample set is small (52), and may not have captured more extreme examples of nitrogen accumulation.



Xanthosoma Sagittifolium
Photograph by Obsidian Soul under CC BY-SA 4.0

Table 3. Biomeconcentration factors (Bf) of phosphorus dynamic accumulators and excluders.

Plant-average 3,706 ppm; 99% confidence interval 3244 – 4168 ppm; 438 samples.

Species Common name Part ppm Bf
Xanthosoma sagittifolium  Malanga Leaf 38,416 10.37
Chenopodium album  Lambsquarter Leaf 36,833 9.94
Momordica charantia  Bitter melon Leaf 33,467 9.03
Equisetum arvense  Horsetail Plant 14,762 3.98
Luffa aegyptiaca Luffa Leaf 14,141 3.82
Sesamum indicum  Sesame Leaf 14,000 3.78
Lactuca sativa  Lettuce Leaf 13,920 3.76
Phaseolus vulgaris Bean Fruit 13,500 3.64
Physalis angulata  Winter Cherry Fruit 13,500 3.64
Cucumis sativus  Cucumber Fruit 12,600 3.40
Phaseolus lunatus  Bean Leaf 360 0.10
Physalis ixocarpa  Tomatillo Fruit 250 0.07
Opuntia ficus-indica  Prickly pear Bud 243 0.07
Ulmus rubra  Slippery elm Bark 220 0.06
Spondias tuberosa  Imbu Fruit 210 0.06
Syzygium cumini  Dulce Fruit 127 0.03
Tabebuia heptaphylla  Pau D’arco Bark 120 0.03
Musa x paradisiaca  Banana Pith 100 0.03
Byrsonima crassifolia  Nance Fruit 100 0.03
Quercus alba  White Oak Bark 64 0.02

The variance in phosphorus levels is high; from 10 x more to 50 x less than average. The large sample set and tight confidence interval qualify these plants well.

Phosphorus acquisition is significantly enhanced by mycorrhizal fungi; the accumulators of Table 3 are all endomycorrhizal associates.



Table 4. Biomeconcentration factors (Bf) of potassium dynamic accumulators and excluders.

Plant-average 22,834 ppm; 99% confidence interval 20414 – 25254 ppm; 347 samples.

Species Common name Part ppm Bf
Lactuca sativa  Lettuce Leaf 121,800 5.33
Cichorium endivia Endive Leaf 96,000 4.20
Chenopodium album  Lambsquarter Leaf 87,100 3.81
Brassica pekinensis  Chinese cabbage Leaf 81,900 3.59
Portulaca oleracea  Purslane Herb 81,200 3.56
Avena sativa  Oats Plant 78,900 3.46
Anethum graveolens  Dill Plant 76,450 3.35
Amaranthus sp.  Pigweed Leaf 73,503 3.22
Cucumis sativus  Cucumber Fruit 72,500 3.18
Brassica chinensis  Bok choy Leaf 69,143 3.03
Vicia faba  Broadbean Fruit 2,670 0.12
Olea europaea Olive Fruit 2,523 0.11
Akebia quinata  Chocolate vine Stem 2,410 0.11
Albizia julibrissin  Mimosa Bark 1,990 0.09
Myrica cerifera  Bayberry Bark 1,960 0.09
Quercus alba  White Oak Bark 1,900 0.08
Tabebuia heptaphylla Pau D’arco Bark 1,850 0.08
Boehmeria nivea  Ramie Plant 1,300 0.06
Elaeagnus umbellatus Russian olive Fruit 1,125 0.05
Aloe vera  Aloe Leaf 850 0.04

As with phosphorus so it is with potassium: mycorrhizae increase potassium uptake. Endomycorrhizal hosts dominate the accumulators and a mix of endo/ecto hosts are found in the excluders.



Table 5. Biomeconcentration factors (Bf) of sulphur dynamic accumulators and excluders.

Plant-average 2,337 ppm; 99% confidence interval 1680 – 2994 ppm; 90 samples.

Species Common name Part ppm Bf
Brassica oleracea Cauliflower Leaf 11,800 5.05
Anethum graveolens  Dill Plant 11,175 4.78
Brassica oleracea Cabbage Leaf 8,750 3.74
Petasites japonicus  Butterbur Plant 7,300 3.12
Urtica dioica  Stinging nettle Leaf 6,665 2.85
Trichosanthes anguina Snakegourd Fruit 6,480 2.77
Portulaca oleracea Purslane Plant 6,300 2.70
Piper nigrum  Black/White pepper Fruit 5,760 2.46
Spinacia oleracea  Spinach Plant 5,700 2.44
Morus alba  White mulberry Leaf 5,600 2.40
Pyrus communis  Pear Fruit 300 0.13
Colocasia esculenta  Taro Leaf 240 0.10
Cucumis melo Canteloupe Fruit 198 0.08
Spondias dulcis  Ambarella Fruit 180 0.08
Solanum melongena  Eggplant Fruit 152 0.07
Psidium guajava  Guava Fruit 140 0.06
Punica granatum  Pomegranate Fruit 120 0.05
Spondias tuberosa  Imbu Fruit 120 0.05
Ananas comosus  Pineapple Fruit 70 0.03
Malus domestica  Apple Fruit 23 0.01

A smaller sample set may have failed to capture the range of sulphur accumulators but high biodiversity is seen. As a general rule, fleshy fruits appear low in sulphur while leafy greens are higher.



Cucurbita foetidissima 
Photography by Curtis Clark, licensed under the Creative Commons Attribution-Share Alike 2.5 Generic license.

Table 6. Biomeconcentration factors (Bf) of calcium dynamic accumulators and excluders.

Plant-average 11,244 ppm; 99% confidence interval 9946 – 12542 ppm; 496 samples.

Species Common name Part ppm Bf
Cucurbita foetidissima  Buffalo gourd Leaf 77,600 6.91
Lycopersicon esculentum  Tomato Leaf 60,800 5.42
Mimulus glabratus  Huaca-mullo Shoot 54,300 4.84
Brassica oleracea Cauliflower Leaf 54,247 4.83
Amaranthus sp.  Pigweed Leaf 53,333 4.75
Boehmeria nivea  Ramie Shoot 46,000 4.10
Justicia pectoralis  Tilo Leaf 44,200 3.94
Liquidambar styraciflua  Sweetgum Stem 42,000 3.74
Plectranthus amboinicus  various Leaf 41,430 3.69
Carya glabra Pignut hickory Shoot 40,700 3.63
Phaseolus coccineus  Scarlet runner bean Fruit 610 0.05
Physalis peruviana  Cape gooseberry Fruit 585 0.05
Malus domestica  Apple Fruit 570 0.05
Allium sativum Garlic Shoot 538 0.05
Musa x paradisiaca  Banana Fruit 460 0.04
Casimiroa edulis  White sapote Fruit 455 0.04
Bixa orellana  Lipstick pod Fruit 450 0.04
Vaccinium corymbosum  Blueberry Fruit 400 0.04
Spondias tuberosa  Inbu Fruit 200 0.02
Theobroma bicolor  Nicaraguan cacao Fruit 184 0.02

Like sulphur, calcium is lower in fruit than vegetative materials. Generally, there is wide diversity of plants found within the accumulators and excluders alike.

Various ‘hungry’ vegetables occur in two or more of the major nutrient tables (2-6). Cauliflower and other brassicas are notable accumulators. These plants are non-mycorrhizal but production can be enhanced with saprobic fungi e.g. Oyster mushrooms and Garden giants; and Trichoderma fungi.




Table 7. Biomeconcentration factors (Bf) of magnesium dynamic accumulators and excluders.

Plant-average 3488 ppm; 99% confidence interval 2983 – 3993 ppm; 287 samples.

Species Common name Part ppm Bf
Carya glabra  Pignut hickory Shoot 24,200 6.94
Carya ovata  Shagbark hickory Shoot 21,600 6.19
Portulaca oleracea  Purslane Herb 18,700 5.36
Phaseolus vulgaris Bean Fruit 18,000 5.16
Avena sativa  Oats Plant 14,800 4.24
Spinacia oleracea  Spinach Plant 11,000 3.15
Tephrosia purpurea  Wild indigo Leaf 10,300 2.95
Trichosanthes anguina  Snake gourd Fruit 9,815 2.81
Prunus serotina Black cherry Leaf 9,600 2.75
Rhus copallina  Winged sumac Leaf 9,600 2.75
Annona reticulata  Custard apple Fruit 630 0.18
Ipomoea batatas  Sweet potato Leaf 620 0.18
Prunus armeniaca  Apricot Fruit 615 0.18
Vaccinium vitis-idaea Cowberry Fruit 600 0.17
Malus domestica  Apple Fruit 478 0.14
Juglans nigra  Black walnut Fruit 440 0.13
Psophocarpus tetragonolobus  Asparagus pea Leaf 346 0.10
Vaccinium corymbosum  Blueberry Fruit 332 0.10
Syzygium jambos  Rose apple Fruit 260 0.07
Spondias tuberosa  Imbu Fruit 90 0.03



Table 8. Biomeconcentration factors (Bf) of silicon dynamic accumulators and excluders.

Plant-average 392 ppm; 99% confidence interval 107 – 677 ppm; 75 samples.

Species Common name Part ppm Bf
Urtica dioica  Stinging nettle Leaf 6,500 16.58
Carya glabra  Pignut hickory Shoot 4,180 10.66
Quercus rubra  Northern red oak Stem 2,422 6.18
Carya ovata  Shagbark hickory Shoot 2,250 5.74
Petroselinum crispum  Parsley Leaf 1,425 3.64
Phaseolus vulgaris Bean Fruit 1,200 3.06
Cucumis sativus  Cucumber Fruit 1,000 2.55
Spinacia oleracea  Spinach Leaf 855 2.18
Lactuca sativa  Lettuce Leaf 800 2.04
Anethum graveolens  Dill Plant 700 1.79
Rosa canina  Dog rose Fruit 25 0.06
Citrus reticulata  Mandarin Fruit 23 0.06
Aloe vera  Aloe Leaf 22 0.06
Juglans nigra  Black walnut Fruit 22 0.06
Pyrus communis  Pear Fruit 20 0.05
Quercus alba  White oak Bark 16 0.04
Rubus idaeus  Raspberry Leaf 13 0.03
Trifolium pratense  Clover Flower 12 0.03
Lobelia inflata  Lobelia Leaf 8 0.02
Foeniculum vulgare  Fennel Fruit 4 0.01



Table 9. Biomeconcentration factors (Bf) of iron dynamic accumulators and excluders.

Plant-average 374 ppm; 99% confidence interval 299 – 449 ppm; 427 samples.

Species Common name Part ppm Bf
Taraxacum officinale  Dandelion Leaf 5,000 13.37
Symphoricarpos orbiculatus  Buckbush Stem 4,400 11.76
Valerianella locusta Corn salad Plant 4,143 11.08
Artemisia vulgaris  Mugwort Plant 3,900 10.43
Boehmeria nivea  Ramie Plant 3,500 9.36
Physalis ixocarpa  Tomatillo Fruit 2,974 7.95
Stellaria media  Chickweed Plant 2,530 6.76
Verbascum thapsus  Mullein Leaf 2,360 6.31
Mentha pulegium  European pennyroyal Plant 2,310 6.18
Carthamus tinctorius  Safflower Flower 2,200 5.88
Musa x paradisiaca  Banana Fruit 25 0.07
Psidium guajava  Guava Fruit 24 0.06
Artocarpus heterophyllus  Jackfruit Fruit 22 0.06
Punica granatum  Pomegranate Fruit 16 0.04
Mauritia flexuosa Morichi palm Fruit 15 0.04
Persea schiedeana  Wild pear Fruit 15 0.04
Mimosa pudica  Touch me not Leaf 14 0.04
Vaccinium corymbosum  Blueberry Fruit 11 0.03
Citrus sinensis  Orange Fruit 8 0.02
Feijoa sellowiana  Brazilian guava Fruit 3 0.01



Valerianella Locusta
Photograph by Kristian Peters under licence CC BY-SA 3.0

Table 10. Biomeconcentration factors (Bf) of boron dynamic accumulators and excluders.

Plant-average 46 ppm; 99% confidence interval 35 – 57 ppm; 122 samples.

Species Common name Part ppm Bf
Valerianella locusta  Corn salad Plant 350 7.61
Prunus domestica  Plum Fruit 255 5.54
Cydonia oblonga  Quince Fruit 160 3.48
Fragaria spp  Strawberry Fruit 160 3.48
Prunus persica  Peach Fruit 150 3.26
Brassica oleracea Cabbage Leaf 145 3.15
Nyssa sylvatica  Black gum Leaf 136 2.96
Taraxacum officinale  Dandelion Leaf 125 2.72
Malus domestica  Apple Fruit 110 2.39
Annona squamosa  Sugar-apple Leaf 107 2.33
Phoenix dactylifera  Date palm Fruit 7 0.15
Averrhoa carambola  Star fruit Fruit 6.8 0.15
Cynara cardunculus Artichoke Flower 5 0.11
Citrullus lanatus  Watermelon Fruit 4 0.09
Olea europaea Olive Fruit 4 0.09
Colocasia esculenta  Taro Leaf 3.6 0.08
Annona muricata  Soursop Fruit 3 0.07
Spondias dulcis  Ambarella Fruit 1.9 0.04
Spondias tuberosa  Imbu Fruit 1.45 0.03
Cucurbita pepo  Pumpkin Fruit 1 0.02



Table 11. Biomeconcentration factors (Bf) of copper dynamic accumulators and excluders.

Plant-average 21 ppm; 99% confidence interval 14 – 29 ppm; 213 samples.

Species Common name Part ppm Bf
Prunus serotina Black cherry Stem 378 17.70
Liquidambar styraciflua  Sweetgum Stem 262 12.27
Nyssa sylvatica  Black gum Leaf 182 8.52
Symphoricarpos orbiculatus  Buckbush Stem 132 6.18
Diospyros virginiana  American persimmon Stem 108 5.06
Lycopersicon esculentum  Tomato Fruit 100 4.68
Brassica oleracea Cabbage Leaf 87 4.07
Sassafras albidum  Sassafras Leaf 79 3.70
Sesamum indicum Sesame Plant 56 2.62
Phaseolus vulgaris Bean Fruit 45 2.11
Vaccinium corymbosum  Blueberry Fruit 4 0.19
Ficus carica  Fig Fruit 3.6 0.17
Brassica pekinensis  Chinese cabbage Leaf 3.15 0.15
Trigonella foenum-graecum  Fenugreek Leaf 3 0.14
Mentha spicata  Spearmint Plant 2 0.09
Punica granatum  Pomegranate Fruit 2 0.09
Vicia faba  Broadbean Fruit 1.7 0.08
Annona muricata  Soursop Fruit 1.6 0.07
Colocasia esculenta  Taro Leaf 1.5 0.07
Genipa americana Genipap Fruit 1 0.05



Table 12. Biomeconcentration factors (Bf) of manganese dynamic accumulators and excluders.

Plant-average 272 ppm; 99% confidence interval 170 – 374 ppm; 242 samples.

Species Common name Part ppm Bf
Quercus alba  White oak Stem 3,800 13 .97
Quercus rubra Northern red oak Stem 3,300 12.13
Carya glabra  Pignut hickory Shoot 3,300 12.13
Nyssa sylvatica  Black gum Leaf 2,730 10.04
Carya ovata  Shagbark hickory Shoot 2,700 9.93
Symphoricarpos orbiculatus Buckbush Stem 2,640 9.71
Juniperus virginiana  Red Cedar Shoot 2,640 9.71
Vaccinium myrtillus  Bilberry Leaf 2,500 9.19
Vaccinium vitis-idaea Cowberry Leaf 2,500 9.19
Liquidambar styraciflua  Sweetgum Stem 2,460 9.04
Ficus carica  Fig Fruit 7 0.03
Aloe vera  Aloe Leaf 6 0.02
Pyrus communis  Pear Fruit 5.55 0.02
Annona cherimola  Cherimoya Fruit 5 0.02
Citrus paradisi  Grapefruit Fruit 5 0.02
Citrus reticulata  Mandarin Fruit 4.6 0.02
Citrullus lanatus  Watermelon Fruit 4 0.01
Artocarpus altilis  Breadfruit Fruit 3.5 0.01
Annona muricata Soursop Fruit 2.7 0.01
Carica papaya  Papaya Fruit 1.1 0.00

More trees appear among the accumulators of micro- compared to macro-nutrients. Access to sufficient quantities of some of the rarer elements in an ecosystem may require the increased depth and density of root-fungal exploration afforded by trees root systems. However, it could also be that, over the same period of time: higher Bf accumulator trees acquisition of a nutrient could be similar or even slower than that of lower Bf, but rapidly growing, herbal counterparts.

Where remediation of a specific pollutant (too much nutrient) is the goal: knowledge of the accumulator’s growth potential over time will help indicate a general time frame for remediation efforts.

Tree litter can be a good source of micronutrients as is shown in the data. By association ectomycorrhizal and lignicolous fungal communities, and litter communities have important roles in micronutrient cycling. Providing chip mulch and/or leaf litter for newly planted trees will help accelerate the restoration of the tree associated biology involved in these processes.

Through collecting nutrient concentration data for a plant species and getting the Bf value we can qualify dynamic accumulators with some certainty. Multiple points of data for singular species will enable better quantification. Much like various plants have different root profiles in the soil, various plants have differing nutrient profiles. If we have two dynamic accumulators (for the same nutrient/s) in close proximity we may create competition; whereas if we identify species with varying nutritional needs we might better identify plants that stack together to increase overall production.

Dynamic accumulators, like their hyperaccumulator counterparts (see Part One), will be useful to mop up specific excesses in soils; or provide specific richness to the diets of humans and animals. They have potential for improving silage, baleage, composting, mulching and other systems designed to capture and redistribute nutrients. Within permaculture systems they add to the palette of functions among the many plants we already employ. The accumulators might readily inhabit and mediate edges where excess nutrients are expected. Some will likely thrive in or proximal to stock enclosures, bird runs, effluent paddocks, ponds, wetlands, and composting and processing areas.

Dean Brown

DC Brown is an ecologist, microbiologist and keen student of permaculture and traditional agriculture. Residing in Auckland, New Zealand, he is still researching and writing a book: Heavy Metal Detox: The Holistic Treatment of Undesirable Elements.


  1. Hi Dean, thanks for the great articles. The information is extremely useful for us. I was wondering your opinion on what would be the best way to make a biofertilzer (for example, a foliar spray) with these dynamic accumulators.

    1. Hi Zac

      Thank you for your interest and please excuse the tardiness of my reply.

      I like to use lactobacillus and trichoderma when creating fertilisers, which also adds these beneficial organisms to the environment when used. Lactobacillus recipes can be found online: I base most recipes using Gil Carandang’s work as a guide, which can be found with a google search. Lactobacillus have a large genome with a whole suite of useful enzymes to break things down.

      Trichoderma can be harvested from the ‘mold’ growing on citrus and coffee grounds. Take some of the white and green powder (spores) off the citrus and the green off the coffee. You only need a little e.g. a couple of grams would do a 44 gallon drum. Trichoderma will aid in decomposition, destroy pathogenic fungi, and elicit a plants response against insect and fungal attack if they are prevalent in the environment. The Trichoderma will produce chitinase, which breaks down chitin – a major constituent of insect exoskeletons and fungal cell walls. High levels of chitinase elicits the plant defense response.

      Add lactobacillus, trichoderma, and water with 5% molasses to just cover your shredded plant materials. Brew with a lid loosely on to allow air exchange. When a bacterial mat (scoby) forms on the surface, and the plant materials are mostly liquid with sludge at the base, you’re good to go. Strain the materials and spray as you would other bio-ferts. About 1:10 fert:water. Compost the solids you’ve strained out or just throw them in some mulch let the worms have it.

      If you do a fish hydrolysate as Gil spells out, and add trichoderma as I spell out… you’ll have an amazing product well superior to anything you can buy in a store.

      And… enjoy!

Leave a Reply

Your email address will not be published. Required fields are marked *

Related Articles

Back to top button